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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 501–550 of 1149 papers

TitleStatusHype
Temporal Grounding of Activities using Multimodal Large Language Models—0
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo BenchmarkCode2
EgoSurgery-Phase: A Dataset of Surgical Phase Recognition from Egocentric Open Surgery VideosCode1
VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long VideosCode2
MMCTAgent: Multi-modal Critical Thinking Agent Framework for Complex Visual Reasoning—0
Hierarchical Action Recognition: A Contrastive Video-Language Approach with Hierarchical Interactions—0
Hawk: Learning to Understand Open-World Video AnomaliesCode3
Streaming Long Video Understanding with Large Language Models—0
MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models—0
Dense Connector for MLLMsCode2
TOPA: Extending Large Language Models for Video Understanding via Text-Only Pre-AlignmentCode1
Anticipating Object State Changes in Long Procedural Videos—0
Open-Vocabulary Spatio-Temporal Action Detection—0
CinePile: A Long Video Question Answering Dataset and Benchmark—0
Challenges in Deploying Long-Context Transformers: A Theoretical Peak Performance Analysis—0
No Time to Waste: Squeeze Time into Channel for Mobile Video UnderstandingCode1
Global Motion Understanding in Large-Scale Video Object Segmentation—0
RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning—0
A Survey on Backbones for Deep Video Action Recognition—0
Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition—0
Vision Mamba: A Comprehensive Survey and TaxonomyCode2
Snippet-Aware Transformer With Multiple Action Elements for Skeleton-Based Action SegmentationCode0
Foundation Models for Video Understanding: A SurveyCode2
WorldQA: Multimodal World Knowledge in Videos through Long-Chain Reasoning—0
How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs—0
Learning text-to-video retrieval from image captioning—0
Open-Set Video-based Facial Expression Recognition with Human Expression-sensitive Prompting—0
MovieChat+: Question-aware Sparse Memory for Long Video Question AnsweringCode4
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense CaptioningCode4
SFMViT: SlowFast Meet ViT in Chaotic WorldCode1
IPAD: Industrial Process Anomaly Detection Dataset—0
From Image to Video, what do we need in multimodal LLMs?—0
Leveraging Temporal Contextualization for Video Action RecognitionCode2
In My Perspective, In My Hands: Accurate Egocentric 2D Hand Pose and Action RecognitionCode0
Task-Driven Exploration: Decoupling and Inter-Task Feedback for Joint Moment Retrieval and Highlight DetectionCode1
Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event AnalysisCode1
Gaze-Guided Graph Neural Network for Action Anticipation Conditioned on Intention—0
A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos—0
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video UnderstandingCode3
SportsHHI: A Dataset for Human-Human Interaction Detection in Sports VideosCode1
Koala: Key frame-conditioned long video-LLM—0
BioVL-QR: Egocentric Biochemical Vision-and-Language Dataset Using Micro QR Codes—0
OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning—0
LongVLM: Efficient Long Video Understanding via Large Language ModelsCode2
MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual TokensCode4
SnAG: Scalable and Accurate Video GroundingCode4
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding—0
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding—0
Instrument-tissue Interaction Detection Framework for Surgical Video Understanding—0
ST-LLM: Large Language Models Are Effective Temporal LearnersCode2
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