Tagged articles

deep learning

1283 articles · Page 8 of 13

Jul 18, 2021 · Artificial Intelligence

Deep Learning Techniques for Sentiment Analysis

The article explains how deep‑learning models, particularly convolutional neural networks with token‑level padding, kernel size three, and max‑pooling, can automatically classify e‑commerce product reviews into eight sentiment categories, offering scalable insight for decision‑making and paving the way for recommendation, QA, and risk‑assessment applications.

Natural Language ProcessingSentiment Analysisconvolutional neural network

0 likes · 9 min read

Deep Learning Techniques for Sentiment Analysis

DataFunTalk

Jul 17, 2021 · Artificial Intelligence

Multi-Objective Modeling for CRM Opportunity Smart Allocation: Iterative Deep Learning Solutions

This article describes the evolution of a multi‑objective deep‑learning framework for automatically assigning CRM opportunities to salespeople, detailing five model versions—from an XGBoost baseline with sample weighting to advanced PLE‑based architectures—while reporting offline and online performance gains in both call‑out and connection‑out conversion rates.

A/B testingCRMdeep learning

0 likes · 33 min read

Multi-Objective Modeling for CRM Opportunity Smart Allocation: Iterative Deep Learning Solutions

Architects' Tech Alliance

Jul 16, 2021 · Artificial Intelligence

AI Chip Landscape: GPUs, FPGAs, and ASICs for Deep Learning

The article explains how artificial intelligence relies on algorithms, compute and data, compares engineering and simulation methods, and details the roles, architectures, performance and energy characteristics of GPUs, FPGAs, and ASICs as the primary hardware accelerators for modern deep‑learning applications.

ASICArtificial IntelligenceFPGA

0 likes · 14 min read

AI Chip Landscape: GPUs, FPGAs, and ASICs for Deep Learning

Kuaishou Tech

Jul 16, 2021 · Artificial Intelligence

Bagua: An Open‑Source Distributed Training Framework for Deep Learning

Bagua is a distributed training framework co‑developed by Kuaishou and ETH Zürich that combines algorithmic and system‑level optimizations—such as decentralized, asynchronous, and compressed communication—to achieve up to 60% higher performance than existing frameworks like PyTorch‑DDP, Horovod, and BytePS across various AI workloads.

BaguaDistributed TrainingGPU scaling

0 likes · 15 min read

Bagua: An Open‑Source Distributed Training Framework for Deep Learning

DataFunTalk

Jul 10, 2021 · Artificial Intelligence

Multi‑Business Ranking Modeling and Optimization in Meituan Search

This article presents Meituan's multi‑business search ranking system, describing the challenges of mixed‑business queries, the layered architecture, the evolution of multi‑business quota models (MQM‑V1/V2) and multi‑business ranking networks (MBN‑V1‑V4), experimental results, and future research directions.

Meituandeep learningmulti‑business modeling

0 likes · 16 min read

Multi‑Business Ranking Modeling and Optimization in Meituan Search

MaGe Linux Operations

Jul 8, 2021 · Artificial Intelligence

TensorFlow vs PyTorch 2.x: Which AI Framework Wins in 2021?

An in‑depth comparison of TensorFlow 2.x and PyTorch 1.8 highlights new features, deployment options like TensorFlow Lite and PyTorch Mobile, coding style differences, and practical guidance on choosing the right deep‑learning library for various projects and skill levels.

PyTorchTensorFlowdeep learning

0 likes · 6 min read

TensorFlow vs PyTorch 2.x: Which AI Framework Wins in 2021?

Python Programming Learning Circle

Jul 6, 2021 · Artificial Intelligence

Understanding ResNet and Building It from Scratch with PyTorch

This article explains the motivation behind residual networks, describes the architecture of ResNet including residual blocks and skip connections, lists available Keras implementations, and provides a step‑by‑step PyTorch tutorial with complete code to construct and test ResNet‑50/101/152 models.

CNNPyTorchResNet

0 likes · 10 min read

Understanding ResNet and Building It from Scratch with PyTorch

Python Programming Learning Circle

Jul 3, 2021 · Artificial Intelligence

Automatic PDF Slide Transcription Using Deep Learning OCR

This article demonstrates how to automatically convert PDF slide decks into editable markdown text by first converting each page to images, then applying a deep‑learning OCR pipeline (CTPN for detection and CRNN for recognition) with Python code examples, achieving high transcription accuracy.

OCRPDF conversiondeep learning

0 likes · 6 min read

Automatic PDF Slide Transcription Using Deep Learning OCR

TiPaiPai Technical Team

Jul 2, 2021 · Artificial Intelligence

How Graph Neural Networks Revolutionize Arbitrary‑Shaped Text Detection

This article reviews two recent computer‑vision approaches—DRRG and STKM—that combine CNN backbones with graph‑based relational reasoning and self‑attention to achieve state‑of‑the‑art detection of arbitrarily shaped text in images.

CNNGCNGraph Neural Network

0 likes · 11 min read

How Graph Neural Networks Revolutionize Arbitrary‑Shaped Text Detection

TiPaiPai Technical Team

Jul 2, 2021 · Artificial Intelligence

How ContourNet and CenterNet Revolutionize Text Detection

This article explains the challenges of scene text detection and introduces two state‑of‑the‑art models, ContourNet and CenterNet, detailing their architectural innovations, loss functions, and how they overcome issues like extreme aspect ratios and anchor‑based inefficiencies.

CenterNetContourNetObject Detection

0 likes · 7 min read

How ContourNet and CenterNet Revolutionize Text Detection

21CTO

Jun 28, 2021 · Artificial Intelligence

How Multimodal AI Detects Pornographic Videos: Image & Audio Fusion Explained

This article outlines a multimodal AI framework for detecting pornographic video content by combining image and audio analysis, detailing the challenges of visual and speech-based recognition, describing the DCNet and RANet model architectures, fusion strategies, and reporting experimental accuracy of 93.4% on a 3k test set.

AIAudio ClassificationPornography Detection

0 likes · 5 min read

How Multimodal AI Detects Pornographic Videos: Image & Audio Fusion Explained

TiPaiPai Technical Team

Jun 28, 2021 · Artificial Intelligence

How Deep Learning Unwarps Twisted Document Images: DocUNet & DewarpNet Explained

This article reviews two end‑to‑end deep‑learning approaches—DocUNet (CVPR 2018) and DewarpNet (ICCV 2019)—for correcting warped document images, detailing their network architectures, synthetic data generation, loss functions, experimental results, and the remaining challenges in document dewarping.

OCRcomputer visiondeep learning

0 likes · 14 min read

How Deep Learning Unwarps Twisted Document Images: DocUNet & DewarpNet Explained

Python Crawling & Data Mining

Jun 27, 2021 · Artificial Intelligence

How SQLFlow Turns Simple SQL Queries into Powerful AI Models

SQLFlow is an open‑source platform that lets users build and run machine‑learning and deep‑learning models directly from SQL statements, lowering the barrier for business analysts to apply AI by abstracting complex pipelines into familiar database queries.

Artificial IntelligenceSQLSQLFlow

0 likes · 8 min read

How SQLFlow Turns Simple SQL Queries into Powerful AI Models

Alimama Tech

Jun 24, 2021 · Artificial Intelligence

One‑Stage Training for Generative Adversarial Networks (OSGAN): Methodology and Efficiency Analysis

The OSGAN method introduced by Alibaba’s Mama team and Prof. Song Ming‑Li merges generator and discriminator updates into a single stage, cutting GAN training time by roughly 1.5‑1.7× while maintaining performance, and is validated on symmetric and asymmetric DCGANs with open‑source code.

GaNOSGANOne‑Stage Training

0 likes · 10 min read

One‑Stage Training for Generative Adversarial Networks (OSGAN): Methodology and Efficiency Analysis

Tencent Advertising Technology

Jun 22, 2021 · Artificial Intelligence

Technical Insights and Solution Strategies from the Tencent Advertising Algorithm Competition – Video Ad Track

The article outlines the Tencent Advertising Algorithm Competition’s video ad challenge, details the paper submission guidelines, and shares a participant’s step‑by‑step technical approach—including baseline experiments, model re‑implementation with Paddle, multimodal feature extraction, optimizer choices, and future improvement directions—providing practical AI insights for multimedia video classification.

Tencent competitiondeep learningfeature extraction

0 likes · 7 min read

Technical Insights and Solution Strategies from the Tencent Advertising Algorithm Competition – Video Ad Track

Baidu Geek Talk

Jun 21, 2021 · Artificial Intelligence

Detecting Pornographic Videos with Dual‑Modal AI: Images + Audio

This article presents a technical overview of a multimodal AI framework that combines image and audio analysis to identify pornographic video content, detailing model architectures, feature extraction methods, and experimental results achieving 93.4% accuracy on a 3,000‑sample test set.

Audio Analysisdeep learningimage recognition

0 likes · 6 min read

Detecting Pornographic Videos with Dual‑Modal AI: Images + Audio

TiPaiPai Technical Team

Jun 17, 2021 · Artificial Intelligence

From Pixels to Words: The Evolution and Challenges of Text Detection

This article traces the origins, unique difficulties, method classifications, and current advancements of scene text detection, highlighting how AI has enabled computers to read images and the ongoing research to improve accuracy, speed, and multilingual support.

AIOCRcomputer vision

0 likes · 8 min read

From Pixels to Words: The Evolution and Challenges of Text Detection

JD Tech

Jun 17, 2021 · Artificial Intelligence

MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi‑Task Learning

The paper introduces MTrajRec, a Seq2Seq multi‑task learning framework that simultaneously restores low‑sampling‑rate GPS trajectories to high‑sampling‑rate and aligns them to the road network, achieving more accurate and efficient trajectory recovery for downstream applications such as navigation and travel‑time estimation.

KDD 2021Seq2Seqdeep learning

0 likes · 9 min read

MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi‑Task Learning

DataFunTalk

Jun 12, 2021 · Artificial Intelligence

An Introduction to Machine Learning: Concepts, Learning Path, and Knowledge System

This article provides a comprehensive overview of machine learning, explaining core AI terminology, distinguishing statistics, statistical learning, and machine learning, outlining a three‑part learning roadmap covering mathematical foundations, algorithms, and Python programming practice, and offering curated resources for building a solid knowledge system.

AI FundamentalsLearning Roadmapdeep learning

0 likes · 8 min read

An Introduction to Machine Learning: Concepts, Learning Path, and Knowledge System

JD Tech

Jun 12, 2021 · Artificial Intelligence

DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction Using Aerial Images and Trajectories

The paper presents DeepDualMapper, a gated‑fusion deep network that combines aerial imagery and vehicle trajectory data to automatically generate high‑precision maps, detailing its architecture, gated and refinement modules, and experimental validation on three city datasets.

Trajectory DataU-Netaerial imagery

0 likes · 7 min read

DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction Using Aerial Images and Trajectories

Meituan Technology Team

Jun 10, 2021 · Artificial Intelligence

Deep Position-wise Interaction Network for CTR Prediction

The Meituan team introduces DPIN, a three‑module deep network that jointly models ads and their positions to mitigate position bias in CTR prediction, achieving up to 2.98% AUC improvement, 2.25% higher CTR and 2.15% RPM gains while keeping latency modest, and is applicable to broader ranking tasks.

AdvertisingCTR predictionDPIN

0 likes · 24 min read

Deep Position-wise Interaction Network for CTR Prediction

Xianyu Technology

Jun 9, 2021 · Artificial Intelligence

Applying Visual AI Techniques for Image Quality and Duplicate Detection in Xianyu Marketplace

By deploying large‑scale visual AI—including a ResNet‑101 classifier, ArcFace‑trained matching features, clustering‑based sub‑category refinement, and product‑level image indexing—Xianyu’s marketplace dramatically improves image quality, removes duplicates, enhances search relevance and feed diversity, and filters non‑compliant content.

computer visiondeep learningduplicate detection

0 likes · 16 min read

Applying Visual AI Techniques for Image Quality and Duplicate Detection in Xianyu Marketplace

WeChat Backend Team

Jun 7, 2021 · Artificial Intelligence

How WeChat’s TFCC Boosts Deep Learning Inference Performance Across Platforms

The TFCC framework, developed by WeChat's backend team, delivers high‑performance, easy‑to‑use, and universal deep‑learning inference by supporting numerous ONNX and TensorFlow operations, optimizing model structures, constants, and operators, and providing a versatile runtime and math library for both CPU and GPU platforms.

ONNXTFCCTensorFlow

0 likes · 8 min read

How WeChat’s TFCC Boosts Deep Learning Inference Performance Across Platforms

58 Tech

Jun 4, 2021 · Artificial Intelligence

Architecture and Evolution of the 58 Intelligent Q&A Chatbot System

This article details the design, iterative development, and performance optimizations of 58's AI‑driven intelligent Q&A chatbot, covering its overall three‑layer architecture, the QABot, TaskBot, and answer‑recommendation modules, as well as dynamic strategy adjustment, caching mechanisms, and real‑world deployment results.

AINLPchatbot

0 likes · 16 min read

Architecture and Evolution of the 58 Intelligent Q&A Chatbot System

Alibaba Cloud Native

Jun 3, 2021 · Artificial Intelligence

How Weibo Boosted Deep Learning Training Speed 18× with Fluid and JindoRuntime

Weibo’s deep learning platform faced severe latency and stability issues when accessing massive small‑file datasets via a compute‑storage‑separated architecture, so the team adopted the CNCF Fluid project with JindoRuntime, implementing a distributed cache that leverages POSIX interfaces, dramatically improving data locality, reducing HDFS load, and achieving up to 18‑fold training speedups while raising success rates from 37 % to 98 %.

Data CachingDistributed TrainingFluid

0 likes · 15 min read

How Weibo Boosted Deep Learning Training Speed 18× with Fluid and JindoRuntime

Tencent Music Tech Team

Jun 1, 2021 · Artificial Intelligence

TDQA: A No-Reference Deep Learning Based Video Quality Assessment Algorithm for Live Streaming

TDQA is a no‑reference, deep‑learning video quality assessment algorithm designed for live‑streaming, built on a large subjectively annotated dataset and an end‑to‑end architecture with fine‑tuned backbones, achieving state‑of‑the‑art accuracy and sub‑second inference for real‑time quality monitoring and pipeline optimization.

No-ReferenceTDQAdataset construction

0 likes · 15 min read

TDQA: A No-Reference Deep Learning Based Video Quality Assessment Algorithm for Live Streaming

DataFunTalk

May 31, 2021 · Artificial Intelligence

Intelligent Transportation Search Ranking: From Business Rules to Personalized Ranking Models

This article presents the challenges of travel‑related product search, explains why traditional rule‑based sorting is insufficient, and describes how Alibaba Flypig’s team built a deep‑learning based personalized ranking system—including architecture, model variants, experimental results, and future optimization directions—to improve conversion rates for flight and ticket searches.

AIPersonalized RecommendationRanking Models

0 likes · 9 min read

Intelligent Transportation Search Ranking: From Business Rules to Personalized Ranking Models

Architects Research Society

May 30, 2021 · Artificial Intelligence

Artificial Intelligence vs. Machine Learning: Definitions, History, and Key Differences

This article explains the origins, definitions, and evolving relationship between artificial intelligence and machine learning, highlighting their historical milestones, core concepts, and how modern applications like deep learning, neural networks, and recommendation systems illustrate their intertwined development.

AIDefinitionsTechnology

0 likes · 8 min read

Artificial Intelligence vs. Machine Learning: Definitions, History, and Key Differences

Python Programming Learning Circle

May 29, 2021 · Artificial Intelligence

Comparing PyTorch 1.8 and TensorFlow 2.5: New Features, Use Cases, and Choosing the Right Framework

This article reviews the latest releases of PyTorch 1.8 and TensorFlow 2.5, outlining their new functionalities, ecosystem tools such as TensorFlow.js, Lite, and TFX, as well as PyTorch Mobile and Lightning, and provides guidance on selecting the most suitable framework for different deep‑learning projects.

Artificial IntelligencePyTorchTensorFlow

0 likes · 7 min read

Comparing PyTorch 1.8 and TensorFlow 2.5: New Features, Use Cases, and Choosing the Right Framework

Kuaishou Tech

May 29, 2021 · Artificial Intelligence

Speaker-Aware Module for Single-Sample Voice Conversion (SAVC)

The paper presents a speaker‑aware module (SAM) that enables high‑quality voice conversion using only a single utterance of the target speaker, addressing the small‑data challenge in speech timbre transfer and achieving state‑of‑the‑art performance on the Aishell‑1 benchmark.

LPCNetSpeech Synthesisdeep learning

0 likes · 12 min read

Speaker-Aware Module for Single-Sample Voice Conversion (SAVC)

Cyber Elephant Tech Team

May 26, 2021 · Artificial Intelligence

Can GANs Eliminate Motion Blur? A Deep Learning Approach to Image Deblurring

This article reviews a GAN‑based deep learning method for removing motion blur from images, covering the problem definition, related work, the multi‑scale generator and discriminator architecture, loss functions, the GoPro dataset, and experimental results that demonstrate clear visual improvements.

GaNMotion Blurcomputer vision

0 likes · 11 min read

Can GANs Eliminate Motion Blur? A Deep Learning Approach to Image Deblurring

Kuaishou Tech

May 24, 2021 · Artificial Intelligence

BCNet: A Bilayer Instance Segmentation Network for Occlusion‑Aware Object Detection

The paper proposes BCNet, a lightweight bilayer instance segmentation network that explicitly models occluder and occludee relationships by treating each region of interest as two overlapping layers, achieving significant performance gains on COCO, COCOA and KINS datasets under heavy occlusion.

bilayer networkcomputer visiondeep learning

0 likes · 10 min read

BCNet: A Bilayer Instance Segmentation Network for Occlusion‑Aware Object Detection

Alimama Tech

May 20, 2021 · Artificial Intelligence

How Alibaba’s AI Powers Brand Risk Detection: Models, Data, and Results

This article details Alibaba's AliMama brand risk identification system, covering the challenges of counterfeit detection, the construction of large‑scale brand datasets, the design of classification, logo detection, and variation models, their optimization, evaluation metrics, and future directions for AI‑driven brand protection.

AIAlibabaObject Detection

0 likes · 22 min read

Kuaishou Tech

May 17, 2021 · Industry Insights

How Kuaishou Delivered Real‑Time Deep‑Learning Voice Conversion on PC

Kuaishou becomes the first company to deploy a deep‑learning‑based real‑time voice‑conversion system on PC clients, delivering stable, natural‑sounding transformed speech with sub‑200 ms latency, and the article analyzes industry methods, technical challenges, and the four‑module architecture that made it possible.

Kuaishouaudio processingdeep learning

0 likes · 10 min read

How Kuaishou Delivered Real‑Time Deep‑Learning Voice Conversion on PC

Tencent Tech

May 13, 2021 · Artificial Intelligence

Seeing Inside the Black Box: Visualizing Neural Network Training and Adversarial Threats

This article explains how neural networks work, walks through the step‑by‑step training process of a convolutional model, showcases vivid visualizations of each layer, and demonstrates how tiny adversarial perturbations can dramatically alter predictions, highlighting the importance of AI security.

AI securityCNN visualizationadversarial examples

0 likes · 6 min read

Seeing Inside the Black Box: Visualizing Neural Network Training and Adversarial Threats

Kuaishou Tech

May 10, 2021 · Artificial Intelligence

Semantic Image Matting: Integrating Alpha Pattern Semantics into the Matting Framework

The article presents Semantic Image Matting, a novel approach that incorporates 20 semantic Alpha pattern categories into the matting pipeline via semantic Trimap, region‑based classifiers, multi‑class discriminators, and learnable gradient loss, achieving state‑of‑the‑art results on multiple benchmarks.

Semantic Segmentationalpha patternscomputer vision

0 likes · 11 min read

Semantic Image Matting: Integrating Alpha Pattern Semantics into the Matting Framework

DeWu Technology

Apr 30, 2021 · Artificial Intelligence

Deep Learning Based Image Aesthetic Quality Assessment

The paper presents a deep‑learning approach that uses an ImageNet‑pretrained CNN to predict full human rating distributions for images via an Earth Mover’s Distance loss, trained on the AVA dataset, and demonstrates accurate assessment of aesthetic factors such as tone, contrast, and composition.

AVA datasetCNNEMD loss

0 likes · 8 min read

Deep Learning Based Image Aesthetic Quality Assessment

JD Cloud Developers

Apr 30, 2021 · Artificial Intelligence

How Face Keypoint Localization Advances Under Masked Conditions: Insights from JD AI’s 3rd Competition

The JD AI Institute and ICME2021 concluded their third face keypoint localization contest, emphasizing efficient masked‑face detection to aid COVID‑19 contact tracing, attracting top universities and tech firms, expanding data scale, and tightening model efficiency constraints to push the field forward.

AI competitioncomputer visiondeep learning

0 likes · 4 min read

How Face Keypoint Localization Advances Under Masked Conditions: Insights from JD AI’s 3rd Competition

DataFunTalk

Apr 29, 2021 · Artificial Intelligence

Path‑based Deep Network (PDN) for E‑commerce Recommendation Recall

This paper proposes a Path‑based Deep Network (PDN) that combines similarity‑index and embedding‑based retrieval paradigms to model user‑item interactions via Trigger Net and Similarity Net, achieving significant improvements in click‑through rate, GMV, and diversity on Taobao’s homepage feed.

PDNRecallclick-through-rate

0 likes · 21 min read

Path‑based Deep Network (PDN) for E‑commerce Recommendation Recall

Tencent Music Tech Team

Apr 26, 2021 · Artificial Intelligence

Tencent Music Multimedia R&D Center Announces Acceptance of Papers on Large-Scale Singer Recognition and Audio Embeddings at IJCNN and ICASSP 2021

Tencent Music’s Multimedia R&D Center celebrated its first appearances at IJCNN and ICASSP 2021 by having two papers accepted—one presenting large‑scale singer recognition via deep metric learning and the other describing user‑driven audio embeddings for content‑based music recommendation—highlighting the team’s expanding expertise across diverse music‑recognition technologies and future research directions.

Audio EmbeddingICASSPIJCNN

0 likes · 8 min read

Tencent Music Multimedia R&D Center Announces Acceptance of Papers on Large-Scale Singer Recognition and Audio Embeddings at IJCNN and ICASSP 2021

360 Quality & Efficiency

Apr 23, 2021 · Artificial Intelligence

Deep Learning for Code Analysis: Workflow, Program Representation, Code2vec Architecture, and Limitations

This guide examines how deep learning techniques are applied to large‑scale code analysis, covering the technical workflow, program representations such as token sequences and AST paths, the code2vec architecture, its advantages, current limitations, and potential applications like code summarization and similarity detection.

AI for software engineeringcode analysiscode2vec

0 likes · 9 min read

Deep Learning for Code Analysis: Workflow, Program Representation, Code2vec Architecture, and Limitations

360 Quality & Efficiency

Apr 16, 2021 · Artificial Intelligence

Applying YOLOv5 Object Detection for Black, Color, and Blank Screen Classification in Video Frames

This article presents a method that replaces manual visual inspection with an automated YOLOv5‑based object detection pipeline to classify video frames as normal, colorful, or black screens, detailing data annotation, training, loss calculation, inference code, and showing a 97% accuracy improvement over ResNet.

Object DetectionPythonYOLOv5

0 likes · 11 min read

Applying YOLOv5 Object Detection for Black, Color, and Blank Screen Classification in Video Frames

58UXD

Apr 12, 2021 · Artificial Intelligence

How 58.com Built an AI Designer: From Smart Cutout to Intelligent Creative Platform

This article chronicles 58.com’s journey from a small brainstorming room to a full‑scale AI design platform, detailing the development of smart cutout, the BASNet segmentation model, custom loss functions, template editing, and the measurable business impact of the AI designer.

AI designBASNetcomputer vision

0 likes · 15 min read

How 58.com Built an AI Designer: From Smart Cutout to Intelligent Creative Platform

DataFunTalk

Apr 12, 2021 · Artificial Intelligence

Comprehensive Survey of Graph Neural Networks: 15 Key Review Papers and Resources

This article compiles and summarizes fifteen influential survey papers on Graph Neural Networks, covering their models, applications, datasets, benchmarks, challenges, and future directions, while providing links to the original PDFs and highlighting distinctions between small and large-scale graph learning.

deep learninggraph learningmachine learning

0 likes · 20 min read

Comprehensive Survey of Graph Neural Networks: 15 Key Review Papers and Resources

Youku Technology

Apr 8, 2021 · Artificial Intelligence

Champion Solution of Media AI Alibaba Entertainment Video Object Segmentation Challenge

The Youku AI team won the Media AI Alibaba Entertainment Video Object Segmentation Challenge by enhancing the STM model with a spatial‑constrained memory reader, ASPP‑HRNet refinement, ResNeSt‑101 backbone, and a multi‑stage training pipeline, while also devising an unsupervised framework that combines DetectoRS detection, HRNet mask refinement, STM‑based association, and key‑frame optimization to achieve 95.5% test score on a large, richly annotated video dataset.

Spatial Memory NetworksUnsupervised VOScomputer vision

0 likes · 13 min read

Champion Solution of Media AI Alibaba Entertainment Video Object Segmentation Challenge

DataFunTalk

Apr 3, 2021 · Artificial Intelligence

A Survey of User Behavior Sequence Modeling for Search and Recommendation Advertising

User behavior sequence modeling, crucial for search and recommendation advertising ranking, has evolved from simple pooling to attention, RNN, capsule, and Transformer architectures, with industrial applications across e‑commerce, social, video, and music platforms, and future directions include time‑aware, multi‑dimensional, and self‑supervised approaches.

AttentionSequence ModelingTransformer

0 likes · 24 min read

A Survey of User Behavior Sequence Modeling for Search and Recommendation Advertising

Huawei Cloud Developer Alliance

Apr 3, 2021 · Artificial Intelligence

Can AI Bring Loved Ones Back? Exploring Digital Immortality

Amid the convergence of Qingming and Easter, this article examines how AI technologies—from voice synthesis to digital avatars—are being used to preserve and “resurrect” deceased loved ones, exploring real-world examples, technical methods, ethical dilemmas, and the future potential of digital immortality.

AIdeep learningdigital avatars

0 likes · 9 min read

Can AI Bring Loved Ones Back? Exploring Digital Immortality

MaGe Linux Operations

Mar 29, 2021 · Artificial Intelligence

Mastering PyTorch Visualization: TensorBoard and Visdom Guide

This tutorial explains how to install, launch, and use TensorBoard and Visdom with PyTorch, providing step‑by‑step commands, code examples for logging training metrics, and visualizing images and plots to monitor deep‑learning experiments.

PyTorchPythonTensorBoard

0 likes · 6 min read

Mastering PyTorch Visualization: TensorBoard and Visdom Guide

iQIYI Technical Product Team

Mar 26, 2021 · Artificial Intelligence

Insights into OCR Technology at iQIYI: Development, Challenges, and Applications

iQIYI’s OCR journey, explained by researcher Harlon, covers the evolution from separate detection and recognition pipelines to end‑to‑end models, key algorithms like CTPN, DB and CRNN, large‑scale simulated training, diverse video‑text applications, and future goals such as mobile deployment and tighter NLP integration.

AIOCRPaddleOCR

0 likes · 21 min read

Insights into OCR Technology at iQIYI: Development, Challenges, and Applications

21CTO

Mar 23, 2021 · Artificial Intelligence

How AI is Revolutionizing Monkey Identification: The Tri‑AI System

Researchers at Northwestern University have developed the Tri‑AI system, a deep‑learning facial recognition platform that accurately identifies individual golden snub‑nosed monkeys in the wild, achieving 94% precision and enabling non‑invasive monitoring, data collection, and broader applications across multiple animal species.

AIanimal monitoringdeep learning

0 likes · 9 min read

How AI is Revolutionizing Monkey Identification: The Tri‑AI System

Kuaishou Tech

Mar 22, 2021 · Artificial Intelligence

Unified Model Compression Framework (UMEC) for Efficient Recommendation Systems

The paper introduces UMEC, a unified model compression framework that jointly optimizes feature embedding and prediction modules under resource constraints, achieving up to three‑fold compression of recommendation models without sacrificing accuracy, and demonstrates superior performance on multiple benchmark datasets.

AIUMECdeep learning

0 likes · 9 min read

Unified Model Compression Framework (UMEC) for Efficient Recommendation Systems

Amap Tech

Mar 22, 2021 · Artificial Intelligence

Visual Technology for Automated POI Name Generation: STR, Text Detection, and Naming Practices

Amap’s visual‑technology pipeline automatically generates and updates POI names by crowdsourcing street‑level images, applying deep‑learning scene‑text recognition, dual‑branch classification of text attributes, and a BERT‑plus‑graph‑attention model that selects and orders recognized text, achieving about 95 % naming accuracy.

Name GenerationOCRPOI

0 likes · 14 min read

Visual Technology for Automated POI Name Generation: STR, Text Detection, and Naming Practices

DataFunTalk

Mar 17, 2021 · Artificial Intelligence

Deep Ranking Model Evolution and Applications in Taobao Live: DBMTL, DMR, and RUI Ranking

This article presents a comprehensive overview of Taobao Live's deep ranking system evolution, detailing the DBMTL multi‑task learning framework, the two‑tower DMR matching‑ranking architecture, and the RUI Ranking refer‑item model, together with their offline formulas, online deployment scenarios, and measured performance gains across click‑through, watch‑time, and conversion metrics.

AITaobao Livedeep learning

0 likes · 27 min read

Deep Ranking Model Evolution and Applications in Taobao Live: DBMTL, DMR, and RUI Ranking

DataFunTalk

Mar 14, 2021 · Artificial Intelligence

A Review of Medical Domain Sentiment Analysis: Interpretability, Contextual Aspect‑Sentiment Relations, Noisy Labels, and Domain Lexicon Construction

This article reviews recent research on medical sentiment analysis, covering interpretability of neural models, contextual aspect‑sentiment interactions, strategies for handling noisy labels, and methods for building domain‑specific sentiment lexicons, highlighting challenges and proposed solutions.

Sentiment Analysisaspect based sentimentdeep learning

0 likes · 19 min read

A Review of Medical Domain Sentiment Analysis: Interpretability, Contextual Aspect‑Sentiment Relations, Noisy Labels, and Domain Lexicon Construction

DeWu Technology

Mar 12, 2021 · Industry Insights

How Do Recommendation Systems Rank Items? A Deep Dive into Models and Strategies

This article explains the architecture and ranking process of modern recommendation systems, covering the two-stage pipeline of candidate generation and ranking, the evolution from rule‑based methods to logistic regression, GBDT, wide‑and‑deep, and deep learning models, and discusses challenges such as feature non‑linearity, multi‑objective optimization, and the need for post‑ranking interventions.

GBDTLogistic Regressiondeep learning

0 likes · 15 min read

How Do Recommendation Systems Rank Items? A Deep Dive into Models and Strategies

JD Cloud Developers

Mar 12, 2021 · Artificial Intelligence

How Deep Meta‑Learning Boosts Spatio‑Temporal Sales Forecasting for Retail

This article summarizes a AAAI 2021 paper that introduces a deep meta‑learning framework with an amortization network to generate spatio‑temporal representations, enabling accurate retail sales predictions across regions and time periods, especially during high‑volume shopping festivals.

Meta-LearningSales Forecastingdeep learning

0 likes · 8 min read

How Deep Meta‑Learning Boosts Spatio‑Temporal Sales Forecasting for Retail

MaGe Linux Operations

Mar 11, 2021 · Artificial Intelligence

What’s New in PyTorch 1.8? Key Features, APIs, and Performance Boosts

PyTorch 1.8, released by the PyTorch team, bundles over 3,000 commits since 1.7, introducing AMD ROCm support, enhanced Python function conversion, stable FFT and linear‑algebra APIs, complex‑tensor autograd, distributed‑training improvements, new mobile tutorials, performance tools, and several prototype features.

PyTorchVersion Releasedeep learning

0 likes · 6 min read

What’s New in PyTorch 1.8? Key Features, APIs, and Performance Boosts

Alibaba Cloud Native

Mar 5, 2021 · Artificial Intelligence

How Alluxio Supercharges Cloud Deep Learning: Benchmarks, Architecture, and Tuning

This article examines why accelerating cloud‑based deep learning is essential, presents benchmark results comparing GPU generations and distributed training, introduces Alluxio as a distributed memory‑level cache, details its architecture on Kubernetes, and offers concrete tuning strategies to overcome I/O bottlenecks and boost training performance.

AIAlluxioKubernetes

0 likes · 16 min read

How Alluxio Supercharges Cloud Deep Learning: Benchmarks, Architecture, and Tuning

Tencent Cloud Developer

Mar 4, 2021 · Artificial Intelligence

WeChat OCR: Implementation of Image Text Extraction Feature

WeChat’s 8.0 update introduced an OCR pipeline that first quickly detects text in images, classifies the image type, applies a lightweight multi‑language detection network and a MobileNetV3‑based DBNet recognizer with a multi‑task CTC/Attention model, then merges results via a rule‑based layout analyzer to deliver accurate, well‑formatted extracted text across diverse languages and document types.

DBNetOCROptical Character Recognition

0 likes · 13 min read

WeChat OCR: Implementation of Image Text Extraction Feature

DataFunTalk

Mar 2, 2021 · Artificial Intelligence

Multi-Objective Optimization with MMoE for Taobao "Lying Flat" Channel

This article presents the design and implementation of a multi‑objective optimization framework using Multi‑gate Mixture‑of‑Experts (MMoE) to improve click‑through, conversion, and purchase behaviors in Taobao's "Lying Flat" home‑goods recommendation channel, detailing model variants, feature engineering, loss weighting, and online A/B test results.

CTRCVRMMoE

0 likes · 10 min read

Multi-Objective Optimization with MMoE for Taobao "Lying Flat" Channel

DataFunTalk

Feb 26, 2021 · Artificial Intelligence

Fine‑Grained Sentiment Analysis and Opinion Quadruple Extraction: Methods, Tasks, and Applications

This article introduces the concepts, tasks, and recent advances in text sentiment analysis, focusing on attribute‑level sentiment (TG‑ABSA) and opinion‑quadruple extraction, describing unsupervised, reading‑comprehension, and multi‑task deep‑learning approaches, their implementation on Huawei Cloud, experimental results, and future research directions.

NLPSentiment Analysisaspect‑based sentiment

0 likes · 20 min read

Fine‑Grained Sentiment Analysis and Opinion Quadruple Extraction: Methods, Tasks, and Applications

ITPUB

Feb 25, 2021 · Artificial Intelligence

How 58.com Scales Voice Quality Inspection with AI-Powered Architecture

This article details the AI-driven intelligent voice quality inspection system built by 58.com, covering its background, multi‑layer architecture, speech recognition, role and tag identification, backend services, and the resulting efficiency gains for large‑scale call‑center operations.

AINatural Language Processingcall center automation

0 likes · 15 min read

How 58.com Scales Voice Quality Inspection with AI-Powered Architecture

Kuaishou Large Model

Feb 25, 2021 · Artificial Intelligence

How Kuaishou’s AI‑Powered Beauty Engine Transforms Real‑Time Video

This article details Kuaishou Y‑tech’s Gorgeous beauty platform, covering traditional smoothing, advanced skin‑tone effects, AI‑driven blemish removal, clarity enhancement, local facial tuning, and the UNet‑based GorgeousGAN that delivers one‑click high‑definition beauty for live‑stream and short‑video applications.

AI Beautycomputer visiondeep learning

0 likes · 13 min read

How Kuaishou’s AI‑Powered Beauty Engine Transforms Real‑Time Video

DataFunTalk

Feb 15, 2021 · Artificial Intelligence

Deep Tree Matching (TDM): Evolution and Practice in Large-Scale Retrieval at Alibaba

This article explains Alibaba's Deep Tree Matching (TDM) technology, covering the challenges of large‑scale match retrieval, the progression from classic two‑stage recall to tree‑based indexing, max‑heap tree modeling, beam‑search retrieval, and the joint model‑index learning across TDM 1.0, 2.0, and 3.0, highlighting significant offline and online performance gains and future research directions.

AlibabaBeam Searchdeep learning

0 likes · 15 min read

Deep Tree Matching (TDM): Evolution and Practice in Large-Scale Retrieval at Alibaba

DataFunTalk

Feb 13, 2021 · Artificial Intelligence

Multi-Channel Deep Interest Modeling for 58.com Home Page Recommendations

This article details how 58.com tackled the challenges of multi‑business recommendation on its home page by developing a dual‑channel deep interest model, introducing customized feature‑crossing, optimizing training and online performance, and exploring multi‑channel extensions for broader scenario adaptation.

AIdeep learningfeature engineering

0 likes · 20 min read

Multi-Channel Deep Interest Modeling for 58.com Home Page Recommendations

DataFunTalk

Feb 10, 2021 · Artificial Intelligence

Deep Learning Based Search Ranking Optimization for 58.com Rental Services

This article describes how 58.com’s rental platform leverages deep learning models such as Wide&Deep, DeepFM, DCN, DIN, and DIEN to improve search ranking, detailing data pipelines, feature engineering, model iteration, multi‑task training, prediction optimizations, and resulting online performance gains.

deep learningfeature engineeringmodel optimization

0 likes · 27 min read

Deep Learning Based Search Ranking Optimization for 58.com Rental Services

Open Source Linux

Feb 8, 2021 · Operations

How to Set Up Docker with NVIDIA GPU for Deep Learning on Linux

This guide walks you through installing NVIDIA drivers, CUDA, and nvidia-docker2 on a Linux host and configuring Docker containers to access the GPU, including verification steps and sample TensorFlow and PyTorch commands.

CUDADockerLinux

0 likes · 8 min read

How to Set Up Docker with NVIDIA GPU for Deep Learning on Linux

DataFunTalk

Feb 4, 2021 · Artificial Intelligence

Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) for Session‑Based Recommendation

The article introduces the CA‑TCN model, which combines cross‑session item graphs, a temporal convolutional network, and a session‑context graph to capture both item‑level and session‑level cross‑session influences, achieving state‑of‑the‑art performance on benchmark session‑based recommendation datasets.

Graph Neural NetworkTemporal Convolutional Networkcross-session modeling

0 likes · 17 min read

Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) for Session‑Based Recommendation

DataFunTalk

Feb 3, 2021 · Artificial Intelligence

Towards Best Possible Deep Learning Acceleration on the Edge – A Compression-Compilation Co-Design Framework

The lecture presented by Assistant Professor Yanzhi Wang introduces a compression‑compilation co‑design framework (CoCoPIE) that achieves real‑time deep‑learning inference on edge devices through novel pruning and quantization techniques, delivering up to 180× speedup without accuracy loss.

AIdeep learningedge computing

0 likes · 5 min read

Towards Best Possible Deep Learning Acceleration on the Edge – A Compression-Compilation Co-Design Framework

Amap Tech

Feb 1, 2021 · Artificial Intelligence

AMAP-TECH Algorithm Competition: Dynamic Road Condition Analysis Using In-Vehicle Video

The AMAP‑TECH competition challenged participants to infer real‑time road conditions from in‑vehicle video, prompting the authors to combine lane‑wise vehicle detection with LightGBM and later an end‑to‑end DenseNet‑GRU model, augment data, ensemble five networks, and achieve a 0.7237 F1 score while outlining future deployment and research directions.

computer visiondeep learningfeature engineering

0 likes · 15 min read

AMAP-TECH Algorithm Competition: Dynamic Road Condition Analysis Using In-Vehicle Video

JD Tech Talk

Jan 28, 2021 · Artificial Intelligence

Spatial‑Temporal Graph Diffusion Network for City Traffic Flow Forecasting

This article introduces a hierarchical graph neural network model that jointly captures multi‑scale temporal patterns and global spatial context for urban traffic flow prediction, demonstrates its superiority over existing methods on multiple public datasets, and validates each component through extensive ablation studies.

AttentionGraph Neural Networkdeep learning

0 likes · 8 min read

Spatial‑Temporal Graph Diffusion Network for City Traffic Flow Forecasting

DataFunTalk

Jan 25, 2021 · Artificial Intelligence

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

This article reviews the development of Zhihu's search system, describing the transition from early GBDT ranking to deep neural networks, the introduction of multi‑objective and position‑bias‑aware learning‑to‑rank methods, context‑aware techniques, end‑to‑end training, personalization, and future research directions.

DNNGBDTdeep learning

0 likes · 17 min read

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

DataFunTalk

Jan 20, 2021 · Artificial Intelligence

Techniques for Reducing the Computational Complexity of Large-Scale Graph Neural Networks

This article presents an overview of graph neural networks, explains their computational framework, analyzes space and time complexities, and proposes ten practical strategies—including edge avoidance, dimensionality reduction, selective iteration, memory baking, distillation, partitioning, sparse computation, routing, and cross-sample feature sharing—to significantly lower the cost of large‑scale GNN processing.

Computational Complexitydeep learninglarge-scale

0 likes · 14 min read

Techniques for Reducing the Computational Complexity of Large-Scale Graph Neural Networks

DeWu Technology

Jan 18, 2021 · Artificial Intelligence

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

The recall stage, the first filtering step after candidate generation, transforms intuitive attribute‑based shortcuts into sophisticated matrix‑factorization and embedding methods—such as dual‑tower and tree‑based models—enabling fast, personalized, diverse candidate selection for real‑time recommendation pipelines.

Recallcollaborative filteringdeep learning

0 likes · 13 min read

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

Python Crawling & Data Mining

Jan 11, 2021 · Artificial Intelligence

Unlock Text from Images: A Hands‑On Guide to EasyOCR in Python

This article explains what OCR is, introduces the EasyOCR Python library, shows how to install it, walks through step‑by‑step usage with code examples, and summarizes the underlying deep‑learning techniques powering the library.

EasyOCROCRPython

0 likes · 6 min read

Unlock Text from Images: A Hands‑On Guide to EasyOCR in Python

DataFunTalk

Jan 7, 2021 · Artificial Intelligence

User Preference Mining and Modeling Practices at Beike

This article introduces the concept of user preference mining, discusses challenges such as accurate expression, interpretability, and high-dimensional preferences, reviews statistical and model-based approaches including weighting, decay, XGBoost, DNN, LSTM, Seq4Rec, and Deep Interest Network, and describes their practical implementation at Beike.

BeikeLSTMdeep learning

0 likes · 19 min read

User Preference Mining and Modeling Practices at Beike

Sohu Tech Products

Jan 6, 2021 · Artificial Intelligence

Overview of Main Model Compression and Acceleration Techniques: Structural Optimization, Pruning, Quantization, and Knowledge Distillation

This article reviews four mainstream model compression and acceleration methods—structural optimization, pruning, quantization, and knowledge distillation—explaining their principles, implementations, and performance, and presents practical examples such as DistillBERT, TinyBERT, and FastBERT with comparative results.

AIdeep learningknowledge distillation

0 likes · 14 min read

Overview of Main Model Compression and Acceleration Techniques: Structural Optimization, Pruning, Quantization, and Knowledge Distillation

DataFunTalk

Jan 4, 2021 · Artificial Intelligence

Personalized Computing‑Power Allocation for Alibaba Display Advertising: Transformers Engine and DCAF Algorithm

The article presents Alibaba's display‑advertising team’s three‑stage computing‑power efficiency evolution, introduces the DCAF personalized power‑allocation algorithm with its Lagrangian formulation, and describes the AllSpark dynamic‑control framework that together enable a flexible, resource‑aware Transformers engine achieving significant business gains during high‑traffic events.

Online AdvertisingResource AllocationSystem Optimization

0 likes · 21 min read

Personalized Computing‑Power Allocation for Alibaba Display Advertising: Transformers Engine and DCAF Algorithm

Didi Tech

Dec 29, 2020 · Artificial Intelligence

Evolution and Challenges of Perception in L4 Autonomous Driving

The article traces L4 autonomous-driving perception from early rule-based point-cloud methods through data-driven deep-learning models to emerging self-learning, multi-task systems, and highlights four key hurdles—model generalization and explainability, robust multi-sensor fusion, real-time compute limits, and proper uncertainty handling—calling for integrated AI, engineering, and data solutions.

AIcomputer visiondeep learning

0 likes · 12 min read

Evolution and Challenges of Perception in L4 Autonomous Driving

JD Tech Talk

Dec 29, 2020 · Artificial Intelligence

Robust Spatio-Temporal Purchase Prediction via Deep Meta Learning

The paper proposes a deep meta‑learning framework that generates spatio‑temporal representations for retail sales forecasting, especially during large shopping festivals, by combining amortization networks, shared statistical structures, and alternating spatial‑temporal training to achieve robust and accurate predictions despite scarce historical data.

Meta-LearningSales Forecastingdeep learning

0 likes · 9 min read

Robust Spatio-Temporal Purchase Prediction via Deep Meta Learning

21CTO

Dec 22, 2020 · Artificial Intelligence

Explore tinygrad: A Minimalist Deep Learning Framework Under 1000 Lines

tinygrad, an open‑source autograd tensor library by George Hotz, offers a compact PyTorch‑like experience in fewer than 1000 lines, with easy installation, GPU support via PyOpenCL, full EfficientNet inference, and extensible optimizers for rapid neural‑network prototyping.

AIAutograddeep learning

0 likes · 6 min read

Explore tinygrad: A Minimalist Deep Learning Framework Under 1000 Lines

Python Crawling & Data Mining

Dec 22, 2020 · Artificial Intelligence

Create Stunning Video Ghosting Effects with PaddlePaddle’s DeepLabV3p Model

Learn how to generate cinematic ghosting effects in videos by leveraging PaddlePaddle’s PaddleHub deep learning library and the pretrained deeplabv3p_xception65 model for semantic segmentation, with step‑by‑step code, environment setup, and practical testing on classic martial‑arts footage.

Ghost EffectPaddlePaddlePython

0 likes · 7 min read

Create Stunning Video Ghosting Effects with PaddlePaddle’s DeepLabV3p Model

DataFunTalk

Dec 17, 2020 · Artificial Intelligence

Context‑Aware Re‑ranking in Industrial Recommendation Systems: Design and Practice of a List Retrieval System

The article presents a comprehensive study of re‑ranking in large‑scale industrial recommendation pipelines, identifies four key challenges—context awareness, permutation specificity, computational complexity, and business constraints—and proposes a two‑stage List Retrieval System that combines fast sequence search and a generative re‑ranking network with a deep context‑wise model, achieving significant online gains across multiple Taobao feed scenarios.

Context-AwareIndustrial AIdeep learning

0 likes · 28 min read

Context‑Aware Re‑ranking in Industrial Recommendation Systems: Design and Practice of a List Retrieval System

DataFunSummit

Dec 14, 2020 · Artificial Intelligence

LightSeq: High‑Performance Open‑Source Inference Engine for Transformers, GPT and Other NLP Models

This article introduces LightSeq, an open‑source, GPU‑accelerated inference engine that dramatically speeds up Transformer‑based models such as BERT and GPT by up to 14× over TensorFlow, supports multiple decoding strategies, integrates seamlessly with major deep‑learning frameworks, and provides detailed performance benchmarks and technical optimizations.

LightSeqNLPTransformer

0 likes · 15 min read

LightSeq: High‑Performance Open‑Source Inference Engine for Transformers, GPT and Other NLP Models

Tencent Cloud Developer

Dec 14, 2020 · Artificial Intelligence

Game AI SDK: Overview, Architecture, and Usage

Tencent’s open‑source Game AI SDK provides a versatile automation testing platform—supporting a wide range of game genres and mobile/PC apps—by integrating environment simulation, configurable tools, image‑recognition modules, and deep‑learning algorithms (DQN and IM) into a unified, user‑friendly workflow for training and executing AI agents.

Automation TestingSDKTensorFlow

0 likes · 17 min read

Game AI SDK: Overview, Architecture, and Usage

Meituan Technology Team

Dec 10, 2020 · Artificial Intelligence

Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) for Session‑Based Recommendation

The Cross‑Session Aware Temporal Convolutional Network (CA‑TCN) combines a cross‑session item graph, a dilated temporal convolutional network, and a session‑context graph to capture both global cross‑session signals and positional order, achieving state‑of‑the‑art recommendation performance on benchmarks and slated for deployment in Meituan’s e‑commerce platforms.

Graph Neural NetworkTemporal Convolutional Networkcross-session modeling

0 likes · 17 min read

Huawei Cloud Developer Alliance

Dec 10, 2020 · Artificial Intelligence

Can AI Revolutionize Waste Sorting? Market Trends, Challenges, and Fast‑Track Solutions

This article explores how AI is being applied to waste classification—from smart trash cans and autonomous garbage trucks to deep‑learning models—while highlighting data‑labeling hurdles, model selection pitfalls, and how platforms like Huawei Cloud ModelArts can streamline development.

AIModelArtsdeep learning

0 likes · 6 min read

Can AI Revolutionize Waste Sorting? Market Trends, Challenges, and Fast‑Track Solutions

Programmer DD

Dec 5, 2020 · Artificial Intelligence

Revive Vintage Photos with AI: Guide to Bringing-Old-Photos-Back-to-Life

This article introduces the AI‑powered "Bringing-Old-Photos-Back-to-Life" project, explains its requirements, provides step‑by‑step commands for full‑pipeline restoration, scratch detection, global restoration, and face enhancement, and shares the Colab demo and GitHub repository for hands‑on experimentation.

AI image restorationColab demoPython

0 likes · 4 min read

Revive Vintage Photos with AI: Guide to Bringing-Old-Photos-Back-to-Life

DataFunSummit

Dec 3, 2020 · Artificial Intelligence

GAN Fundamentals, Variants, and Practical Applications in Image Style Transfer and Handwriting Font Generation

This article provides a comprehensive overview of Generative Adversarial Networks, covering their original formulation, training dynamics, loss functions, major variants such as DCGAN and WGAN, and practical implementations for image‑to‑image translation, style transfer, and handwriting font synthesis at Laiye Technology.

GaNGenerative Adversarial Networkscomputer vision

0 likes · 28 min read

GAN Fundamentals, Variants, and Practical Applications in Image Style Transfer and Handwriting Font Generation

Kuaishou Large Model

Dec 3, 2020 · Artificial Intelligence

Kuaishou Y‑Tech’s Real‑Time, High‑Precision Facial & Body Keypoint Detection Explained

Y‑Tech’s in‑house keypoint detection system powers Kuaishou’s beauty and effect filters across live streaming, video creation, and editing by leveraging lightweight deep‑learning models, extensive multi‑scenario data collection, and specialized handling of occlusion, enabling real‑time, robust facial and body landmark tracking on diverse mobile devices.

beauty filterscomputer visiondeep learning

0 likes · 10 min read

Kuaishou Y‑Tech’s Real‑Time, High‑Precision Facial & Body Keypoint Detection Explained

360 Quality & Efficiency

Nov 27, 2020 · Artificial Intelligence

Image Similarity Detection Methods: Hashing, Histograms, Feature Matching, BOW+K‑Means, and CNN‑Based Approaches

This article reviews common image similarity detection techniques—including hash-based methods (aHash, pHash, dHash), histogram comparison, feature matching with ORB and SIFT/SURF, bag‑of‑words with K‑Means, and CNN‑based VGG16 features—detailing their algorithms, Python implementations, performance characteristics, and practical considerations.

computer visiondeep learningfeature extraction

0 likes · 15 min read

Image Similarity Detection Methods: Hashing, Histograms, Feature Matching, BOW+K‑Means, and CNN‑Based Approaches

New Oriental Technology

Nov 23, 2020 · Artificial Intelligence

A Seq2Seq Deep Learning Approach for Recognizing Mathematical Formulas in Images

This article presents a deep‑learning Seq2Seq model that converts images of mathematical formulas—including matrices, equations, fractions, and radicals—into LaTeX sequences with over 95% accuracy, detailing data preparation, LaTeX normalization, model architecture, training, inference, and post‑processing techniques.

Formula RecognitionLaTeXOCR

0 likes · 9 min read

A Seq2Seq Deep Learning Approach for Recognizing Mathematical Formulas in Images

DataFunSummit

Nov 22, 2020 · Artificial Intelligence

An Overview of NVIDIA Merlin Recommendation System Framework and Its Deep Learning Components

This article introduces NVIDIA's Merlin recommendation system framework, detailing its three core components—NVTabular for feature engineering, HugeCTR for high‑performance CTR model training, and Triton for inference—while discussing common pipeline challenges, performance advantages, and example implementations for deep‑learning‑based recommender models.

AIHugeCTRNVIDIA Merlin

0 likes · 12 min read

An Overview of NVIDIA Merlin Recommendation System Framework and Its Deep Learning Components

iQIYI Technical Product Team

Nov 20, 2020 · Artificial Intelligence

How iQIYI’s AI Coloring Engine Supercharges Animation Production

iQIYI’s AI‑driven coloring engine uses a GAN‑based model with attention and semantic reference modules to automate line‑art coloring, delivering consistent styles across frames, cutting coloring time to 0.7 seconds per image and reducing labor costs by about 30% in animation pipelines.

AIAnimationGaN

0 likes · 5 min read

How iQIYI’s AI Coloring Engine Supercharges Animation Production

Sohu Tech Products

Nov 18, 2020 · Artificial Intelligence

Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms

This article explains the fundamentals of seq2seq neural machine translation models, covering encoder‑decoder architecture, word embeddings, context vectors, RNN processing, and the attention mechanism introduced by Bahdanau and Luong, with visual illustrations and reference links for deeper study.

Neural Machine TranslationRNNSeq2Seq

0 likes · 11 min read

Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms

DeWu Technology

Nov 18, 2020 · Artificial Intelligence

Evolution and Technical Analysis of Dewu Photo Search

Dewu Photo Search evolved from a limited Aliyun‑based prototype to a self‑developed pipeline using EfficientNet detection and 128‑dim embeddings, boosting top‑1 shoe accuracy over 100 % and overall precision by up to 41 %, while reducing latency and improving scalability despite remaining stability challenges.

deep learningfeature extractionimage search

0 likes · 10 min read

Evolution and Technical Analysis of Dewu Photo Search

DataFunTalk

Nov 16, 2020 · Artificial Intelligence

Deep Semantic Relevance and Multimodal Video Search at Alibaba Entertainment

The presentation by Alibaba Entertainment's senior algorithm expert details the challenges of video search in the 4G/5G era and describes a comprehensive framework covering business overview, relevance and ranking, multimodal retrieval, deep semantic modeling, dataset construction, and practical deployment techniques.

Information RetrievalMultimodaldeep learning

0 likes · 27 min read

Deep Semantic Relevance and Multimodal Video Search at Alibaba Entertainment

Suning Technology

Nov 14, 2020 · Artificial Intelligence

Designing Real-Time AI Algorithms for Unmanned Retail Stores

This lecture details the end‑to‑end AI architecture for unmanned stores, covering algorithm module selection, calibration, face recognition, multi‑task detection, tracking, recommendation, data collection, augmentation, model training, and GPU‑accelerated deployment to achieve real‑time performance and high accuracy.

Data AugmentationReal-time AIdeep learning

0 likes · 15 min read

Designing Real-Time AI Algorithms for Unmanned Retail Stores

JD Cloud Developers

Nov 4, 2020 · Artificial Intelligence

How Cloud Trade Fairs Use AI to Power Smart Recommendations

This article explains how a cloud‑based trade fair leverages AI techniques—including user and item profiling, multi‑level caching with Caffeine and Redis, and a Deep Interest Network model with attention mechanisms—to deliver personalized, high‑performance recommendations for exhibitors, buyers, and individual users.

AICachingcloud trade fair

0 likes · 15 min read

How Cloud Trade Fairs Use AI to Power Smart Recommendations

Didi Tech

Nov 3, 2020 · Artificial Intelligence

Advances in Single‑Channel Speech Separation and Target Speaker Extraction with Iterative Refined Adaptation

The article surveys recent advances in single‑channel speech separation and target‑speaker extraction, explains the encoder‑separator‑decoder framework, compares frequency‑ and time‑domain methods, highlights models such as SpEx+, DPRNN‑Spe, and introduces Iterative Refined Adaptation, which iteratively improves speaker embeddings to boost SI‑SDR performance and enables effective speaker‑suppression for applications like in‑vehicle voice interaction.

AIaudio signal processingdeep learning

0 likes · 13 min read

Advances in Single‑Channel Speech Separation and Target Speaker Extraction with Iterative Refined Adaptation