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Apr 16, 2026 · Artificial Intelligence

CPL++: A Self‑Aware, Self‑Correcting Framework for Weakly Supervised Visual Grounding

The CPL++ framework equips weakly supervised visual grounding models with confidence‑aware pseudo‑label learning, self‑supervised association correction, and dynamic validation, enabling the model to detect and amend erroneous region‑query links during training, which yields absolute performance gains of 1–6 % across five benchmark datasets.

Computer VisionVisual GroundingWeak Supervision

0 likes · 9 min read

CPL++: A Self‑Aware, Self‑Correcting Framework for Weakly Supervised Visual Grounding

DataFunSummit

Sep 11, 2024 · Artificial Intelligence

Weak Supervision Machine Learning in Ant Group Business Scenarios

This article presents an overview of weak supervision machine learning techniques applied to Ant Group’s business scenarios, covering an introduction to weak supervision, challenges of modeling with scarce or noisy labels, detailed methodologies for cross‑domain causal effect estimation, multi‑source noisy label denoising, and real‑world application examples.

Weak Supervisioncausal inferencecross-domain

0 likes · 18 min read

Weak Supervision Machine Learning in Ant Group Business Scenarios

Sohu Tech Products

Jul 17, 2024 · Artificial Intelligence

How Weak Supervision Powers Ant Group’s Real‑World AI Challenges

This article presents a comprehensive technical overview of weak‑supervision machine learning at Ant Group, covering its fundamentals, cross‑domain causal effect estimation, strategies for scarce or noisy labels, novel framework components, experimental validation, and practical application scenarios.

AIWeak Supervisioncausal inference

0 likes · 18 min read

How Weak Supervision Powers Ant Group’s Real‑World AI Challenges

DataFunTalk

Jul 12, 2024 · Artificial Intelligence

Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications

This article presents a comprehensive overview of weak supervision machine learning techniques applied to Ant Group's business problems, covering theoretical foundations, cross‑domain causal effect estimation, noisy‑label denoising frameworks, experimental results, and practical use cases such as risk modeling and marketing interventions.

Weak Supervisioncausal inferencecross-domain learning

0 likes · 16 min read

Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications

Network Intelligence Research Center (NIRC)

Oct 23, 2023 · Artificial Intelligence

How Multiple‑Instance Learning Boosts Context Understanding in Video Anomaly Detection

The article reviews the CVPR 2021 MIST framework, explaining how a multiple‑instance pseudo‑label generator and a self‑guided attention encoder work together with sparse continuous sampling to improve context awareness and detection accuracy in weakly‑supervised video anomaly detection.

Attention EncoderComputer VisionMultiple Instance Learning

0 likes · 9 min read

How Multiple‑Instance Learning Boosts Context Understanding in Video Anomaly Detection

Alimama Tech

Nov 9, 2022 · Artificial Intelligence

Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce

The paper introduces a graph‑based weakly supervised contrastive learning framework that uses heterogeneous user‑behavior graphs, e‑commerce‑specific augmentations, and a hybrid fine‑tuning/transfer learning strategy to improve semantic relevance matching between queries and product titles, achieving significant gains on a large‑scale Taobao dataset.

Weak Supervisioncontrastive learninge‑commerce

0 likes · 12 min read

Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce

DataFunSummit

Jun 22, 2022 · Artificial Intelligence

Generating and Applying Social Relationship Graphs for Video Understanding

This talk presents recent research on integrating dynamic analysis and graph machine learning to generate social relationship graphs from video, detailing hierarchical graph convolution networks, multimodal feature fusion, weakly supervised training, experimental results, and applications such as enhanced video retrieval and storyline understanding.

Graph Neural NetworkWeak Supervisionsocial relationship graph

0 likes · 11 min read

Generating and Applying Social Relationship Graphs for Video Understanding

DataFunTalk

May 20, 2022 · Artificial Intelligence

Hierarchical Graph Convolutional Networks for Video Social Relationship Modeling

This article presents a multimodal approach that combines dynamic analysis and graph machine learning to generate and apply social relationship graphs in videos, detailing problem background, graph generation modules, applications such as video retrieval, experimental results, and future research directions.

AIGraph Neural NetworkWeak Supervision

0 likes · 11 min read

Hierarchical Graph Convolutional Networks for Video Social Relationship Modeling

Ctrip Technology

Aug 26, 2021 · Artificial Intelligence

Applying Snorkel Weak Supervision to Automate Event Summaries in Ctrip Customer Service

The article explains how Ctrip’s hotel customer‑service team uses the Snorkel weak‑supervision framework to generate large‑scale labeled data for training models that automatically produce structured event summaries, detailing the workflow, labeling functions, generative and discriminative model training, and performance improvements.

Labeling FunctionsNLPSnorkel

0 likes · 14 min read

Applying Snorkel Weak Supervision to Automate Event Summaries in Ctrip Customer Service

DataFunTalk

Oct 22, 2020 · Artificial Intelligence

Analyzing Video Excitement: Methods, Frameworks, and Applications

This article presents a comprehensive overview of video excitement analysis, covering quality, aesthetics, and narrative factors, describing a multimodal framework with supervised, weakly supervised, and multi‑task models, and illustrating practical applications such as preview generation, clipping, and automatic cover creation.

Multimodal AIWeak Supervisioncontent recommendation

0 likes · 14 min read

Analyzing Video Excitement: Methods, Frameworks, and Applications

Meituan Technology Team

Jul 23, 2020 · Artificial Intelligence

Named Entity Recognition in O2O Search: Background, Technical Choices, and Practical Practices

Meituan’s O2O search relies on a hybrid NER system that combines high‑precision domain dictionaries with BERT‑based models scored by a CRF, built from multi‑source offline mining, accelerated via operator fusion, batching and mixed‑precision, and further enhanced by lattice‑LSTM, knowledge‑infused stages and weak‑supervision, delivering millisecond‑level latency and over‑90% recall.

Dictionary MatchingKnowledge-EnhancedNER

0 likes · 30 min read

Named Entity Recognition in O2O Search: Background, Technical Choices, and Practical Practices

iQIYI Technical Product Team

Jul 10, 2020 · Artificial Intelligence

Video Highlight Analysis Technology Framework

iQIYI’s video highlight analysis framework combines a large supervised dataset, deep label distribution learning, multi‑task training with a canonical‑correlated autoencoder, and a weakly supervised ranking model enhanced by confidence weighting and graph convolution, then fuses these signals to improve highlight detection accuracy.

Weak Supervisiongraph convolutional networksmulti-task learning

0 likes · 17 min read

Video Highlight Analysis Technology Framework

DataFunTalk

Jan 9, 2019 · Artificial Intelligence

Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications

This article introduces reinforcement learning fundamentals, contrasts it with supervised learning, and explores its challenges and advantages in natural language processing, including applications such as text classification, relation extraction from noisy data, and weakly supervised topic segmentation, while summarizing key insights and experimental results.

Weak Supervisionnatural language processingreinforcement learning

0 likes · 11 min read

Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications