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Video Understanding

A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.

Source: Action Detection from a Robot-Car Perspective

Papers

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Showing 801–825 of 1149 papers

TitleStatusHype
Egocentric and Exocentric Methods: A Short Survey—0
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling—0
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition—0
Exploring Anchor-based Detection for Ego4D Natural Language Query—0
Exploring Missing Modality in Multimodal Egocentric Datasets—0
Exploring State Change Capture of Heterogeneous Backbones @ Ego4D Hands and Objects Challenge 2022—0
Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding—0
Extending Video Masked Autoencoders to 128 frames—0
Extensible Hierarchical Method of Detecting Interactive Actions for Video Understanding—0
Real-Time Segmentation Networks should be Latency Aware—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
FaVChat: Unlocking Fine-Grained Facail Video Understanding with Multimodal Large Language Models—0
FAVOR-Bench: A Comprehensive Benchmark for Fine-Grained Video Motion Understanding—0
Fewer Tokens and Fewer Videos: Extending Video Understanding Abilities in Large Vision-Language Models—0
Fill-in-the-Blank: A Challenging Video Understanding Evaluation Framework—0
Fine-Grain Annotation of Cricket Videos—0
Fine-Grained Video Captioning through Scene Graph Consolidation—0
CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval—0
First Place Solution to the Multiple-choice Video QA Track of The Second Perception Test Challenge—0
Flatten: Video Action Recognition is an Image Classification task—0
Flexible Frame Selection for Efficient Video Reasoning—0
FlexSelect: Flexible Token Selection for Efficient Long Video Understanding—0
FocusChat: Text-guided Long Video Understanding via Spatiotemporal Information Filtering—0
Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions—0
Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles—0
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