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Video Recognition

Video Recognition is a process of obtaining, processing, and analysing data that it receives from a visual source, specifically video.

Papers

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Showing 1–25 of 307 papers

TitleStatusHype
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action RecognitionCode0
VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language ModelsCode0
Gameplay Highlights Generation—0
Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos—0
CA^2ST: Cross-Attention in Audio, Space, and Time for Holistic Video Recognition—0
Leveraging LLMs with Iterative Loop Structure for Enhanced Social Intelligence in Video Question Answering—0
BASKET: A Large-Scale Video Dataset for Fine-Grained Skill EstimationCode1
PAVE: Patching and Adapting Video Large Language ModelsCode1
VTD-CLIP: Video-to-Text Discretization via Prompting CLIPCode0
Towards Scalable Modeling of Compressed Videos for Efficient Action Recognition—0
A Simple and Efficient Baseline for Video Action Recognition—0
VideoPure: Diffusion-based Adversarial Purification for Video RecognitionCode0
Action Detail Matters: Refining Video Recognition with Local Action Queries—0
DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments—0
Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video RecognitionCode2
Standardization Trends on Safety and Trustworthiness Technology for Advanced AI—0
MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge TransferCode0
Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal RepresentationsCode5
A Novel Audio-Visual Information Fusion System for Mental Disorders Detection—0
GenRec: Unifying Video Generation and Recognition with Diffusion ModelsCode0
OmniCLIP: Adapting CLIP for Video Recognition with Spatial-Temporal Omni-Scale Feature LearningCode1
VideoMamba: Spatio-Temporal Selective State Space ModelCode1
Purification Of Contaminated Convolutional Neural Networks Via Robust Recovery: An Approach with Theoretical Guarantee in One-Hidden-Layer Case—0
PosMLP-Video: Spatial and Temporal Relative Position Encoding for Efficient Video RecognitionCode0
MeMSVD: Long-Range Temporal Structure Capturing Using Incremental SVD—0
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