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 476–500 of 1149 papers

TitleStatusHype
InstructionBench: An Instructional Video Understanding Benchmark—0
Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understanding—0
AVT: Audio-Video Transformer for Multimodal Action Recognition—0
Aligned Better, Listen Better for Audio-Visual Large Language Models—0
Disentangle and denoise: Tackling context misalignment for video moment retrieval—0
InfiniPot-V: Memory-Constrained KV Cache Compression for Streaming Video Understanding—0
AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual Counting for MLLMs—0
Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling—0
Large Scale Video Representation Learning via Relational Graph Clustering—0
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks—0
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision—0
Dynamic Appearance: A Video Representation for Action Recognition with Joint Training—0
Beyond the Camera: Neural Networks in World Coordinates—0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition—0
Learning an Augmented RGB Representation with Cross-Modal Knowledge Distillation for Action Detection—0
Learning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval—0
Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer—0
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking—0
Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment—0
Learning from Multiple Sources for Video Summarisation—0
Learning Higher-order Object Interactions for Keypoint-based Video Understanding—0
Inductive Attention for Video Action Anticipation—0
Discrete neural representations for explainable anomaly detection—0
Improving Video Model Transfer With Dynamic Representation Learning—0
Improving LLM Video Understanding with 16 Frames Per Second—0
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