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Zhihu Tech Column
Zhihu Tech Column
Oct 10, 2024 · Artificial Intelligence

Massive Multi-Label Text Classification via Semantic Retrieval and Large AI Model

This article presents a method for massive multi-label text classification on Zhihu content by combining a semantic retrieval model with a proprietary large AI model, detailing the challenges of large label spaces, model architecture, loss optimization, and experimental results showing significant accuracy gains.

BGElarge language modelmulti-label classification
0 likes · 16 min read
Massive Multi-Label Text Classification via Semantic Retrieval and Large AI Model
ITPUB
ITPUB
Jun 9, 2022 · Artificial Intelligence

How 58’s Multi‑Label Image Recognition Boosts Semantic Search and Recommendations

This article details the design, data pipeline, model architecture, loss functions, and evaluation metrics of a large‑scale multi‑label image classification system built for 58.com, showing how it improves semantic similarity detection, recommendation, and content moderation across diverse business domains.

Computer VisionDeep Learningasymmetric loss
0 likes · 18 min read
How 58’s Multi‑Label Image Recognition Boosts Semantic Search and Recommendations
58 Tech
58 Tech
Jun 9, 2022 · Artificial Intelligence

Multi‑Label Image Recognition for 58.com: Algorithm Design, Data Construction, and Model Optimization

This article presents a comprehensive study of multi‑label image recognition applied to 58.com’s business scenarios, covering problem motivation, dataset construction, evaluation metrics, mainstream deep‑learning methods, an asymmetric‑loss‑based optimization pipeline, and practical output schemes for recommendation and retrieval.

Computer Visionasymmetric lossdata annotation
0 likes · 17 min read
Multi‑Label Image Recognition for 58.com: Algorithm Design, Data Construction, and Model Optimization
DataFunTalk
DataFunTalk
Dec 9, 2020 · Artificial Intelligence

WeChat Identify: From Object Detection to Large‑Scale Image Search – Technical Overview

This article details the evolution of WeChat’s Identify product, explaining its end‑to‑end image recognition pipeline—including object detection, multi‑label classification, mobile‑side detection, large‑scale retrieval, unsupervised clustering, and system architecture—while showcasing various application scenarios such as product, plant, and landmark recognition.

Computer VisionMobile AIWeChat
0 likes · 12 min read
WeChat Identify: From Object Detection to Large‑Scale Image Search – Technical Overview
DataFunTalk
DataFunTalk
Nov 22, 2020 · Artificial Intelligence

Short Video Analysis in Local Life Scenarios: Techniques and Practices at Meituan

This article presents Meituan's AI-driven short video analysis workflow, covering industry trends, multi‑label video classification, intelligent cover selection, and video generation techniques, while discussing challenges, model building, label expansion, continuous data iteration, and future outlook for video AI in local services.

AIComputer VisionMeituan
0 likes · 16 min read
Short Video Analysis in Local Life Scenarios: Techniques and Practices at Meituan
Amap Tech
Amap Tech
Aug 27, 2019 · Artificial Intelligence

POI Category Tagging: Multi‑Label Classification, Feature Engineering and Model Design

The system tackles POI category tagging as a multi‑label classification problem by engineering textual and non‑textual features, mining click‑log and external samples through active learning, and deploying hierarchical and per‑tag deep textCNN models with feature fusion, achieving over 5 % accuracy gain, ten‑fold speedup, and markedly higher precision and coverage that boost map‑search relevance.

POI taggingTextCNNfeature engineering
0 likes · 19 min read
POI Category Tagging: Multi‑Label Classification, Feature Engineering and Model Design
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 21, 2019 · Artificial Intelligence

How View-Specific Information Boosts Multi-View Multi-Label Learning (SIMM)

This article explains the SIMM algorithm, a multi‑view multi‑label learning method that extracts view‑specific information alongside shared subspace representations, detailing its motivation, architecture, loss functions, experimental results on eight datasets, and how it outperforms existing approaches.

SIMMadversarial learningmulti-label classification
0 likes · 10 min read
How View-Specific Information Boosts Multi-View Multi-Label Learning (SIMM)
Youku Technology
Youku Technology
Aug 12, 2019 · Artificial Intelligence

Interpretation of the Paper “Multi-View Multi-Label Learning with View‑Specific Information Extraction” (SIMM)

The article explains SIMM, a neural‑network framework for multi‑view multi‑label learning that jointly extracts a shared, view‑invariant subspace via adversarial loss and orthogonal view‑specific features, demonstrating superior performance across eight benchmark datasets compared to existing MVML and ML‑kNN methods.

AIadversarial learningmachine learning
0 likes · 11 min read
Interpretation of the Paper “Multi-View Multi-Label Learning with View‑Specific Information Extraction” (SIMM)
Meitu Technology
Meitu Technology
Aug 30, 2018 · Artificial Intelligence

Meitu Introduces a Multi‑Label Short‑Video Classification Dataset for the 2018 AI Challenger

In the 2018 AI Challenger, Meitu co‑organized a new “Real‑Time Short‑Video Classification” track and released the industry’s first multi‑label short‑video dataset of 200,000 mobile‑captured, vertically oriented videos spanning 63 categories and detailed tags for subjects, scenes, actions, and other dimensions, advancing video semantic understanding and AI research.

AI challengeDatasetMeitu
0 likes · 5 min read
Meitu Introduces a Multi‑Label Short‑Video Classification Dataset for the 2018 AI Challenger