Generalized Moment Retrieval (GMR) is introduced as a unified task with the Soccer-GMR benchmark and adapter models that retrieve multiple or zero matching moments from videos.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
years
2026 5representative citing papers
IAD-Unify unifies industrial anomaly segmentation, region-grounded language understanding, and mask-guided generation in one framework using DINOv2 token injection into Qwen3.5, supported by the new Anomaly-56K dataset of 59,916 images.
A pair-centric set-prediction model unifies present HOI detection and multi-horizon anticipation in video by modeling future interactions as residual transitions from current pair states, backed by a temporally corrected benchmark.
PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.
citing papers explorer
-
Retrieving Any Relevant Moments: Benchmark and Models for Generalized Moment Retrieval
Generalized Moment Retrieval (GMR) is introduced as a unified task with the Soccer-GMR benchmark and adapter models that retrieve multiple or zero matching moments from videos.
-
IAD-Unify: A Region-Grounded Unified Model for Industrial Anomaly Segmentation, Understanding, and Generation
IAD-Unify unifies industrial anomaly segmentation, region-grounded language understanding, and mask-guided generation in one framework using DINOv2 token injection into Qwen3.5, supported by the new Anomaly-56K dataset of 59,916 images.
-
Rethinking Video Human-Object Interaction: Set Prediction over Time for Unified Detection and Anticipation
A pair-centric set-prediction model unifies present HOI detection and multi-horizon anticipation in video by modeling future interactions as residual transitions from current pair states, backed by a temporally corrected benchmark.
-
Learning Shared Sentiment Prototypes for Adaptive Multimodal Sentiment Analysis
PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.
- SpotSound: Enhancing Large Audio-Language Models with Fine-Grained Temporal Grounding