A Scottish football match turned comedic when an AI camera misidentified a linesman's shiny bald head as the ball, highlighting challenges in automated sports broadcasting and prompting fan backlash over the technology's shortcomings.
This article describes how Didi’s vehicle‑vision team built an ultra‑low‑power ADAS solution on the JueShi dash‑cam platform, using lightweight detection models, temporal fusion, camera‑calibration techniques and data‑driven optimization to cut rear‑end collision rates by over 11% and improve overall traffic safety.
DiDi’s ultra‑low‑power JueShi ADAS combines lightweight vision models, temporal‑fusion Kalman filtering, and camera‑calibration techniques to deliver real‑time forward‑collision warnings and brake‑light alerts, cutting rear‑end crashes by over 11% and overall accidents by 9% through continuous edge‑AI learning.
This article introduces the fundamentals of object detection, explains the latest YOLOv4 and YOLOv5 architectures, and details the essential components—including data preparation, regularization, backbone, neck, and prediction innovations—along with label smoothing and advanced loss functions for improved detection performance.
This article explains the fundamental principles of image recognition, covering how images are converted to numeric arrays, processed by scanning matrix blocks, and matched against patterns to identify objects such as text, faces, cats, dogs, or mice.
iQIYI’s ACM Multimedia‑accepted paper unveils iCartoonFace, the world’s largest manually annotated cartoon‑face dataset—over 5,000 characters and 400,000 real‑scene images—accompanied by a semi‑automatic collection pipeline and multi‑person training framework, now powering AI services, large‑scale contests and accelerating cartoon‑character recognition research.
Didi’s COVID‑19 mask‑detection system, built on a DFS‑based face detector and an attention‑enhanced ResNet‑50 mask classifier achieving over 99.5 % accuracy, has been deployed in vehicles, open‑sourced, and complemented by top‑ranked results in international visual AI contests, including first place in driver‑gaze prediction and podium finishes in emotion recognition and model‑compression challenges.
This article reviews Kuashou Y‑Tech’s ECCV‑2020 paper on monocular depth estimation, detailing its novel GCB‑SAB network, new HC‑Depth dataset, specialized loss functions and edge‑aware training, and demonstrates superior performance on NYUv2, TUM and real‑world mobile AR applications.
To enhance object detection robustness, the article discusses various data augmentation methods—including rotation, flipping, random cropping, scaling, color jitter, blurring, transparency adjustment, and image partitioning—providing code examples and illustrating their impact on model performance with before‑and‑after results.
SuNing’s fourth‑generation fully digital visual unmanned store combines 3D reconstruction, AI‑driven perception, and modular hardware‑software design to achieve real‑time, all‑scene, all‑time, all‑digital analysis of people, goods, and spaces, enabling precise offline marketing and scalable retail digitization.
Suning’s lecture series details the three‑decade evolution of retail, the company’s 30‑year digital transformation, and the technical architecture of its fourth‑generation fully digital visual unmanned stores that leverage AI, computer vision, and big‑data analytics to revolutionize in‑store operations and customer experience.
This article presents ZTFace, an end‑to‑end face recognition solution that integrates face detection, alignment, feature embedding, verification, anti‑spoofing and attribute recognition using deep learning, details its backbone networks, loss functions, training datasets, experimental results on WIDER FACE and LFW, and demonstrates acceleration with TensorRT.
This guide explains image matching techniques, covering template matching with OpenCV, various matching methods, SIFT feature extraction and description, FLANN-based nearest neighbor matching, homography estimation, practical challenges, and a brief overview of YOLO training, providing code examples and visual illustrations.
The Boundary Content Graph Neural Network (BC‑GNN) introduces a bipartite‑graph framework that jointly refines start/end boundary probabilities and segment‑content confidence, enabling more precise temporal action proposals and achieving state‑of‑the‑art results on ActivityNet‑1.3 and THUMOS14.
Amap uses advanced scene text recognition combining detection and recognition modules, deep learning, data synthesis, and result fusion to automate map data production, achieving state-of-the-art performance and automating the majority of POI and road updates, significantly reducing labor costs.
This article explains how Amap leverages scene text recognition to automate map data production, detailing the evolution from traditional image algorithms to deep‑learning models, the current detection and recognition framework, performance results, and future research directions for handling blur, data scarcity, and semantic understanding.
This article explains how Gaode Maps leverages traditional and deep‑learning based scene text recognition techniques—including character detection, sequence models, data synthesis, and multi‑stage frameworks—to automate POI and road data production with high precision and speed.
The talk explains how Alibaba’s Entertainment Content Operation Platform applies a Partial‑ReID algorithm to overcome the challenges of person re‑identification in heavily edited video content, enabling accurate cross‑shot character matching, richer appearance data, and metrics such as presence, interaction, and storyline for improved video quality assessment.
This survey reviews video action recognition, comparing 3D convolutional networks that jointly model spatial‑temporal cues but are computationally heavy with 2D‑based approaches like TSM and TIN that embed temporal shifts efficiently, and emphasizes how large‑scale pre‑training markedly improves performance despite limited labeled data.
This article explains the principles and implementation of face detection based on statistical methods, detailing Haar feature types, integral image computation, feature normalization, cascade classifiers, and provides step‑by‑step OpenCV code examples for static images, eye detection, and real‑time webcam detection.
The AMAP-TECH algorithm competition invites participants to develop AI models that analyze in-vehicle video sequences to determine dynamic road conditions, offering detailed dataset specifications, evaluation metrics, expert judges, schedule, and prize information for researchers in computer vision and traffic analytics.
This technical talk introduces video object segmentation tasks, reviews leading datasets and state-of-the-art deep learning models, and shares practical network design rules and performance‑boosting techniques, presented by Prof. Wang Xinggang as part of Alibaba's MEDIA AI challenge series.
Alibaba Amap’s AMAP‑TECH competition invites participants to develop AI computer‑vision models that classify real‑time road conditions—smooth, slow, or congested—from short sequences of dash‑cam images, using a labeled dataset of 1,500 training sequences and a weighted F1‑score evaluation, with cash prizes up to ¥60,000.
This comprehensive review explains visual object tracking in computer vision, covering its definition, core sub‑problems of candidate generation, feature extraction, and decision making, system architecture, motion, feature and observation models, algorithm classifications, evaluation metrics, datasets, and recent research trends.
In the fourth Alibaba Digital Media Technology Night Talk, algorithm engineer Liu Xiaolong presents an overview of video‑based temporal event detection, covering its problem background, representative prior works, and the latest research advances within the MEDIA AI Algorithm Challenge series.
This article explains the fundamentals of virtual reality and its distinction from augmented reality, describes VR hardware, outlines depth‑estimation and eye‑tracking methods such as projection, Hough transform, AdaBoost and sample matching, discusses Sobel edge detection, and explores the importance of audio, haptic feedback, and immersive VR applications in education.
This article introduces image matching fundamentals, covering template matching methods, feature-based approaches such as SIFT and FLANN, their implementation details, matching rules, homography transformation, and practical considerations, providing a comprehensive overview for computer vision applications.
The JD ARVR technology department announced two peer‑reviewed papers—one on a novel defocus blur detection network published in Transaction on Multimedia and another on a transductive relation‑propagation network for few‑shot learning accepted at IJCAI 2020—highlighting their advanced AI research and future AR‑VR ecosystem plans.
Gaode’s automated production line combines deep‑learning image recognition, GPS‑enhanced location services, image differencing with semantic filtering, and standardized data‑fusion to continuously refresh China’s national base map, cutting manual effort and costs while delivering real‑time, high‑quality map updates for road traffic infrastructure.
This article explains the AR Cut & Paste prototype by Cyril Diagne, detailing its three‑module architecture, the underlying BASNet and U²‑Net vision models, and provides a step‑by‑step guide—including code snippets and GitHub links—to set up the mobile app, local server, and Photoshop integration.
This tutorial demonstrates how to use Python and the PaddlePaddle deep‑learning platform to automatically remove backgrounds from multiple photos in one step, covering installation, verification, and a concise five‑line code example for batch human segmentation.
ChineseOCR Lite is an open‑source, ultra‑lightweight OCR solution that supports vertical Chinese text, runs on Linux/macOS via ncnn inference, and packs detection, recognition, and angle classification models into a total of just 17 MB, offering fast and accurate scene‑text processing.
Didi’s DGF‑M model, a mask‑aware face‑recognition AI, combines multi‑task training and synthetic data to detect masks with under 0.1 % miss rate and verify identities with up to 99.5 % pass rate at a 0.1 % false‑acceptance rate, and is deployed for driver verification, offered through the Didi Cloud API marketplace, and released as an open‑source solution to aid pandemic‑era security.
In a live Q&A, Alibaba Amap’s chief scientist Ren Xiaofeng explained how computer‑vision algorithms underpin high‑precision map creation, AR navigation, visual localization and sensor fusion, discussed current hardware limits, deep‑learning bottlenecks, 5G’s role, edge‑cloud cooperation, and offered career advice for transitioning researchers.
Jason Mayes, a Google web engineer, open‑sourced a TensorFlow.js demo that removes people from live webcam video in real time using a lightweight MobileNet model, with only about 200 lines of code, and provides GitHub and CodePen links for experimentation.
The iCartoonFace Challenge invites participants to develop efficient algorithms for detecting and recognizing cartoon faces using large, meticulously annotated datasets—50,000 images for detection and nearly 390,000 for recognition—while meeting strict model size and latency limits and submitting detailed methods and code.
This article surveys state‑of‑the‑art deep‑learning techniques for scene text detection and recognition in e‑commerce poster images, detailing models such as CTPN, TextBoxes, SegLink, EAST, and end‑to‑end frameworks, and discusses their architectures, strengths, limitations, and future challenges.
This article explains how Alibaba's Alipay visual vending cabinet leverages 3D synthetic data generation—covering full‑material 3D reconstruction, parametric scene modeling, and photo‑realistic rendering—to rapidly produce high‑quality training images, dramatically cutting cost and accelerating AI model deployment.
Gaode leverages high‑resolution satellite imagery as an active discovery tool for map updates, extracting road, region and building elements through advanced semantic segmentation networks (U‑Net, ASPP, attention, non‑local) and instance‑segmentation pipelines, to accelerate accurate road‑network and building‑block data refreshes while addressing future scalability challenges.
This article presents a system‑level approach that performs deep‑learning inference directly on JPEG frequency components, uses a gating mechanism to select important DCT coefficients, and demonstrates higher accuracy with far lower bandwidth for image classification and instance segmentation tasks.
This paper introduces a method for video multi-label classification that incorporates label correlation features using graph convolutional networks, significantly improving classification performance.
A new AI algorithm jointly developed by Alibaba Security and Zhejiang University learns fine‑grained, attribute‑aware similarity embeddings for fashion images, enabling accurate detection of local design plagiarism and improving retrieval performance across multiple benchmark datasets.
This article explains how UCloud’s AI team leveraged the UAI‑Train and UAI‑Inference platforms to develop, train, and deploy a high‑accuracy face‑mask detection service within a week, detailing the algorithmic approach, challenges, deployment pipeline, and real‑world applications.
This article reviews the rapid development of deep learning, its pivotal role in autonomous driving, outlines end‑to‑end perception‑to‑control pipelines, discusses the strengths and limitations of deep models, and proposes practical strategies such as task decomposition, multi‑method fusion, and sensor integration to improve safety and interpretability.
A curated collection of Reddit‑highlighted 2019 AI research papers, covering theoretical advances, computer‑vision breakthroughs, unsupervised learning methods, and time‑series forecasting, with summaries, key contributions, and direct links to each paper.
This tutorial explains how to detect, calculate, and correct the rotation angle of text in an image using OpenCV, covering image binarization, minimum‑area bounding box extraction, angle adjustment, and affine transformation with clear Python code examples.
This article details the evolution of ground‑marker recognition technology in high‑precision maps, covering challenges of diverse and worn markings, traditional segmentation methods, deep‑learning breakthroughs such as R‑FCN, cascade detectors, corner‑point detection, semantic segmentation, PAnet, and 3‑D point‑cloud approaches, and their impact on accuracy and production efficiency.
The 2019 CCF Big Data & Computing Intelligence Contest’s Video Copyright Detection track, judged by iQIYI, saw 705 teams from 25 countries compete, with Hengyang Data’s VGG‑16‑based solution winning, followed by Boyun Vision, Xiao Jia’s Lao Liang, Hulu Brothers and Beihang University, showcasing diverse deep‑learning and unsupervised approaches for robust video copyright detection.
Alibaba's DAMO Academy details its AI‑driven image cutout system, describing why automated matting is needed, the four‑module pipeline (filtering, classification, detection, segmentation), architectural innovations such as dual decoders and fusion networks, and how these advances enable product‑level batch background removal.
Alibaba's DAMO Academy presents an AI‑driven image cutout system that combines filtering, classification, detection, and advanced segmentation to automate high‑precision matting, improve design efficiency, and unlock new commercial opportunities across e‑commerce and media industries.
WeChat’s iOS Scan‑to‑Identify feature lets users point a camera at any product or scene to instantly retrieve related e‑commerce, encyclopedia or news content, using a four‑pipeline architecture that builds massive annotated and deduplicated databases, advanced RetinaNet‑based detection, multi‑task metric learning, and scalable training, deployment and scheduling platforms, with plans to extend into domains like facial, vehicle and plant recognition.
AI-powered moderation tools now scan text, images, live streams, and short videos, using techniques like TextCNN, Word2Vec, attention‑based classifiers, multi‑label sampling, and real‑time audio analysis to detect pornographic and harmful content, while emphasizing continual model updates and sample collection for both small and large platforms.
This article introduces a complete vehicle license‑plate detection and recognition pipeline—covering image preprocessing, ROI extraction, character segmentation, SVM‑based classification, and a PyQt5 GUI—while also discussing code structure, demo results, and future improvements.
Gaode Maps has progressed image segmentation from early heuristic region splitting to modern deep‑learning pipelines—leveraging FCNs, multi‑task networks, Mask R‑CNN, and specialized losses—to achieve centimeter‑level, instance‑aware mapping of roads, signs, and small objects while pursuing lighter, real‑time models.
The study enhances UI2CODE’s ability to detect tiny UI components by augmenting training data with copied small objects, upgrading the detector from Faster RCNN to FPN and Cascade FPN, and refining box positions with smoothing and projection, achieving superior small‑object mAP/mAR and enabling broader UI parsing applications.
This article provides an in‑depth review of face detection, covering traditional knowledge‑, model‑, feature‑ and appearance‑based approaches, modern deep‑learning methods such as cascade CNN, MTCNN and Facebox, strategies for handling multi‑scale faces, anchor‑box densification, and practical training considerations.
This article introduces the fundamentals of object detection, explains key models such as R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, and showcases how Alibaba's AI technology is applied to photovoltaic quality inspection to boost efficiency and accuracy in industry.
The ICCV 2019 Lightweight Face Recognition Challenge attracted 292 teams and defined four strict FLOP‑ and size‑limited protocols for image and video recognition, with champions employing near‑30 GFLOP EfficientNet‑style backbones, novel loss functions, frame‑fusion, and knowledge‑distilled VarGNet models to balance accuracy and computational budget.
This article explains how Amap leverages computer vision to collect, process, and enhance map data and to deliver low‑cost, real‑time AR navigation, detailing the technical challenges, algorithmic solutions, and the broader mission of connecting the physical world.
India’s prisons are adopting AI-powered video walls and facial‑recognition systems, such as Staqu’s JARVIS platform, to monitor inmate activity, improve security, and generate revenue, while confronting overcrowding, staffing shortages, and violent incidents, illustrating a global shift toward smart‑prison technology.
The talk analyzes sample imbalance and importance in object detection, proposes IoU‑balanced negative sampling and instance‑balanced positive sampling, introduces the Prime Sample concept with Hierarchical Local Rank, and presents Importance‑based Sample Reweighting and Classification‑Aware Regression Loss, achieving consistent mAP gains without extra overhead.
AMap’s ground‑marking recognition has progressed from simple threshold methods to advanced deep‑learning pipelines—including two‑stage R‑FCN, cascade detectors with local regression, corner‑point and segmentation hybrids, and LiDAR‑based 3‑D PointRCNN—achieving over 99 % recall and sub‑5 cm positional accuracy for high‑precision map production.
The Baidu App team showcases how deep‑learning and AI can be deployed on mobile through on‑device and server‑side inference—illustrated by plant‑identification, stylized filters, video subject detection, and AR real‑time translation—while addressing model compression, cross‑platform optimization, and offering a practical guide for engineers.
The article reviews AR navigation lane‑detection, comparing traditional handcrafted visual pipelines with modern deep‑learning segmentation approaches, proposes an efficient multitask network with weight‑allocation and vanishing‑point anchoring, and demonstrates quantized models achieving real‑time, stable performance on low‑power automotive chips while outlining remaining weather, lighting, and road‑condition challenges.
Amap leverages large‑scale visual intelligence—camera‑captured imagery, AI‑driven road‑sign and POI recognition, and compressed AR navigation overlays—to automate map creation, enhance real‑time positioning, and deliver richer travel experiences for its billion‑scale user base.
This article reviews Huawei's ModelArts platform, detailing its data processing, algorithm development, high‑performance training, edge‑cloud model deployment, auto‑learning capabilities, and real‑world use cases such as invisible payment and intelligent waste classification, while outlining future ecosystem prospects.
This article presents a comprehensive overview of UC's information‑flow video tag recognition technology, detailing tag usage scenarios, the end‑to‑end system architecture, multi‑modal feature extraction, advanced deep‑learning models such as NextVlad, behavior and person tagging methods, and future research directions.
The paper presents an efficient scene‑text detector that expands high‑level SSD feature maps and integrates a feature‑pyramid network, using direction‑aware segment‑and‑link predictions to reconstruct arbitrarily long, rotated text, achieving higher recall and precision with real‑time speed and outperforming recent methods on ICDAR benchmarks and a menu‑recognition test.
Didi Maps' chief engineer explains how the team tackled weak GPS signals in large indoor venues by building a 60,000‑square‑meter 3D map, achieving sub‑0.5 m monocular visual localization, and fusing inertial data with Google ARCore to deliver real‑time AR navigation on Android devices.
The article provides a comprehensive overview of Tencent Cloud’s OCR platform, detailing its service architecture, product capabilities, integration methods, performance metrics, engineering improvements, testing automation, and operational considerations, offering developers practical insights into building and deploying OCR solutions on the cloud.
This article explains video object segmentation, outlines the three main research directions—semi‑supervised, interactive, and unsupervised—describes Alibaba’s Moku Lab breakthroughs and competition results, and discusses future plans to improve segmentation in complex scenes.
This study presents a deep‑learning pipeline that employs a Cascade‑RCNN with Feature Pyramid Network to automatically detect and refine modules and their internal elements on Taobao’s 99‑promotion pages, achieving roughly 98 % precision and recall on a thousand‑image validation set and paving the way for broader e‑commerce event applications.
At IJCAI 2019, Alibaba’s MoKu Lab unveiled the Beidou Star platform and an intelligent conversational video search system for end‑to‑end content creation, while its Turing Lab demonstrated security AI such as Green Net, IP Brain, facial‑recognition and Tianyan, complemented by multiple research papers, academic collaborations and new hiring drives.
The talk details how Youku’s Beidou Star AI platform deconstructs the drama “Chang’an Twelve Hours” with NLP, computer‑vision, knowledge graphs and multi‑task deep models to quantify script, character and emotion uncertainty, enabling predictive scoring that lifted the series’ daily index above one million and outlines future hybrid decision‑engine research.
Learn how to create a DIY driver fatigue detection device by integrating Tencent Youtu VisionSeed AI vision module with an STM32 microcontroller, covering hardware assembly, UART communication, algorithm selection, and real‑time alert generation using facial key‑point analysis.
Video Object Segmentation (VOS) is essential for content creation, and Alibaba’s research outlines three main approaches—semi-supervised, interactive, and unsupervised—detailing their algorithms, challenges, evaluation metrics, recent breakthroughs, and future plans to improve accuracy in complex scenes.
The article outlines video object segmentation (VOS), its importance for content creation, and details the three primary research avenues—semi‑supervised, interactive, and unsupervised—while reviewing benchmark metrics, algorithm categories, challenges, and recent advances from Alibaba’s MoKu Lab, including their competition results and future plans.
In the 2019 iQIYI Celebrity Video Identification Challenge, our team secured fifth place by accurately recognizing video identities using mAP scoring, and this article shares the strategies, insights, and experiences of the top‑five teams, emphasizing a straightforward, pragmatic approach championed by iQIYI’s technology product team.
Gaode Maps uses a two-stage machine‑learning pipeline (Faster‑RCNN with shape‑based region proposal networks and fine‑grained classifiers) to detect hundreds of traffic‑sign types in billions of street‑view images, achieving high recall and precision, scalable updates, and near‑real‑time map data refresh.
After a three‑month contest co‑hosted by iQIYI and ACM MM, 255 teams competed on the challenging iQIYI‑VID‑2019 multimodal dataset, and the BUPT Automation School team won with a 91.14% person‑recognition accuracy, advancing the field and enhancing iQIYI’s video recommendation and AI services.
In the second Visual Dialogue Challenge, Alibaba’s AI outperformed ten teams—including Microsoft and Seoul University—achieving a 74.57% accuracy, surpassing the previous record by 16.82% and exceeding human performance, thanks to its novel recursive exploration dialogue model that integrates image recognition, relational reasoning, and natural language understanding.
By integrating YOLOv3 video object detection into Youku’s ad platform, the team replaced traditional subtitle‑based and scene‑based placements with precise object‑level targeting, achieving higher relevance, expanded inventory, and a 20% click‑through increase despite 3.5× higher exposure.
SmileAR, iQIYI’s self‑developed mobile AR platform powered by TensorFlow Lite, delivers real‑time face, body and gesture recognition across iQIYI’s apps through MobileNet‑based models, quantization‑aware training, multi‑task learning and encrypted SDKs, achieving fast, lightweight, cross‑platform AR experiences for millions of users.
The Youku Video Enhancement and Super‑Resolution Challenge invites teams to develop models that restore low‑resolution, noisy video to high‑definition quality using a 10,000‑pair industry dataset, offering up to RMB 100,000 in prizes and a recruitment pathway, with registration open through June 16 and competition phases spanning May to September.
The Youku Video Enhancement and Super‑Resolution Competition challenges teams of up to five to develop 4× upscaling models that also remove noise and compression artifacts, using a 10,000‑pair dataset, with prizes up to ¥100,000 and recruitment opportunities, running from May to September 2019.
This article provides a thorough introduction to image‑search technology, covering its general framework, offline and online components, feature‑extraction evolution, retrieval engine structures, and architectural challenges such as dynamic indexing, feature synchronization, and high‑throughput low‑latency serving.
This article outlines Youku's multimodal video content understanding pipeline, covering business needs, problem decomposition, data construction, model selection, OCR subtitle extraction, scene and action recognition, sample augmentation, noise handling, and multimodal fusion strategies for robust content moderation.
This article presents a comprehensive overview of autonomous driving perception, covering system fundamentals, sensor setups and fusion techniques, onboard processing architecture, and the key technical challenges such as precision‑recall balance, adverse weather, and small‑object detection.
The talk describes Youku’s intelligent production platform, which uses AI and cloud computing to automatically analyze video frames, extract fine‑grained metadata such as scenes, persons, actions and scores, and then generate highlights, vertical clips, annotations and feedback for editors and upstream producers, while addressing challenges like pose‑tracking, graph‑based action classification and future plans for deeper video understanding and open competitions.
This article summarizes a team's practical experience with various image feature extraction methods—including global, local, and CNN features—and demonstrates their use in image retrieval and no‑reference quality assessment through extensive experiments and analysis.
Ren Haibing’s Youku Cognitive Lab talk reviews object segmentation’s motivation, explains semantic and instance concepts, presents UNet‑based and category‑agnostic methods—including fast video segmentation with motion cues—and reports high IoU results while outlining future edge‑aware, label‑free, and non‑online video segmentation research directions.
The article describes an intelligent cover‑image selection system for NetEase News that automatically filters unsuitable illustrations, assesses image quality with a pairwise‑trained deep model across clarity, color and composition, and smartly crops images using aspect‑ratio‑aware object detection, dramatically cutting manual editing and enabling confidence‑based automatic publishing.
The article outlines Tencent Cloud’s face‑recognition technology—from its deep‑learning‑based algorithm training and multi‑layer system architecture, through the YouTu Lab‑powered product suite for detection, analysis, comparison, liveness and search, to real‑world deployments in security, metro transportation and retail, highlighting integration challenges and performance optimizations.
This article provides a concise introduction to image processing, covering its hierarchical structure, fundamental techniques such as classification, detection, segmentation, geometric transformation, and the most widely used libraries and deep‑learning frameworks for newcomers.
This article summarizes a computer‑vision salon where Dr. Ji Yongnan explains imaging pipelines, traditional feature‑based methods, deep‑learning breakthroughs, Tencent Cloud AI services, real‑world case studies, and answers audience questions about machine‑vision versus computer‑vision and data‑scarcity challenges.
The YOUKU‑VSRE 2019 challenge invites researchers to develop state‑of‑the‑art video enhancement and super‑resolution models using the largest, most diverse simulated‑noise dataset, with three competition stages (preliminary, semi‑final, final), cash prizes up to ¥100,000, certificates, and fast‑track recruitment opportunities at Alibaba (Youku).
The CVPR 2019 WAD Autonomous Driving Challenge, hosted in Long Beach, introduces four new tasks—including object‑detection and tracking transfer‑learning tracks using Didi’s massive D²‑City and Berkeley’s BDD100K datasets, plus a large‑scale detection interpolation track—aimed at advancing vision algorithms under diverse, difficult driving conditions, with global teams invited to register by May 31 and winners announced at the workshop on June 17.
This article introduces how machine learning problems are formulated as optimization tasks, explains the construction of objective functions with examples such as linear regression, robust fitting, regularization, and demonstrates various applications ranging from K‑means clustering to image inpainting and 3D reconstruction.
This article provides an extensive review of deep learning techniques applied to computer vision, covering the evolution of CNN architectures, image and video processing tasks, 2.5‑D and 3‑D reconstruction, object detection, segmentation, tracking, SLAM, and various practical applications such as AR, content retrieval, and autonomous driving.
This article explains why simple MD5 checks cannot assess image similarity and introduces two major approaches—image hashing and image feature extraction—detailing their algorithms, practical performance, and how to compare images efficiently using Hamming distance and indexing techniques.
