SOTAVerified

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 676–700 of 1149 papers

TitleStatusHype
Learning Space-Time Semantic Correspondences—0
EPIC Fields: Marrying 3D Geometry and Video UnderstandingCode1
Valley: Video Assistant with Large Language model Enhanced abilitYCode2
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language ModelsCode3
Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment—0
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and BenchmarksCode2
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video UnderstandingCode4
MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning—0
Teacher Agent: A Knowledge Distillation-Free Framework for Rehearsal-based Video Incremental LearningCode0
Action Sensitivity Learning for Temporal Action Localization—0
VideoLLM: Modeling Video Sequence with Large Language ModelsCode1
A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero ShotCode0
Learning Higher-order Object Interactions for Keypoint-based Video Understanding—0
Vehicle Detection and Classification without Residual Calculation: Accelerating HEVC Image Decoding with Random Perturbation Injection—0
Transformer-Based Model for Monocular Visual Odometry: A Video Understanding ApproachCode1
VideoChat: Chat-Centric Video UnderstandingCode4
MH-DETR: Video Moment and Highlight Detection with Cross-modal TransformerCode1
Event-Free Moving Object Segmentation from Moving Ego VehicleCode1
ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System—0
MRSN: Multi-Relation Support Network for Video Action Detection—0
Search-Map-Search: A Frame Selection Paradigm for Action Recognition—0
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision—0
Leveraging triplet loss for unsupervised action segmentationCode1
Therbligs in Action: Video Understanding through Motion Primitives—0
SVT: Supertoken Video Transformer for Efficient Video Understanding—0
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