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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 101–150 of 307 papers

TitleStatusHype
FrameExit: Conditional Early Exiting for Efficient Video RecognitionCode1
Frame Flexible NetworkCode1
Frozen CLIP Models are Efficient Video LearnersCode1
Adapting Short-Term Transformers for Action Detection in Untrimmed VideosCode1
Long Movie Clip Classification with State-Space Video ModelsCode1
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
Audio-Visual Class-Incremental LearningCode1
Real-time Online Video Detection with Temporal Smoothing TransformersCode1
Temporal-attentive Covariance Pooling Networks for Video RecognitionCode1
Glance and Focus Networks for Dynamic Visual RecognitionCode1
Group Contextualization for Video RecognitionCode1
VLG: General Video Recognition with Web Textual KnowledgeCode1
Demonstration of Vector Flow Imaging using Convolutional Neural Networks—0
Image and Video Mining through Online Learning—0
Action Keypoint Network for Efficient Video Recognition—0
Deep Networks With Large Output Spaces—0
HCMS: Hierarchical and Conditional Modality Selection for Efficient Video Recognition—0
Higher-order Network for Action Recognition—0
2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition—0
Multi-Fiber Networks for Video Recognition—0
Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled—0
Defending Against Multiple and Unforeseen Adversarial Videos—0
Hierarchical Action Recognition: A Contrastive Video-Language Approach with Hierarchical Interactions—0
MRET: Multi-resolution Transformer for Video Quality Assessment—0
Audio-Visual Glance Network for Efficient Video Recognition—0
DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection—0
MultAV: Multiplicative Adversarial Videos—0
Audio-Visual Fusion Layers for Event Type Aware Video Recognition—0
GTM: Gray Temporal Model for Video Recognition—0
DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments—0
Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition—0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning—0
Cross-Modal Transferable Adversarial Attacks from Images to Videos—0
Generating Videos with Scene Dynamics—0
Cross-Block Fine-Grained Semantic Cascade for Skeleton-Based Sports Action Recognition—0
Morph: Flexible Acceleration for 3D CNN-based Video Understanding—0
Correlation Net: Spatiotemporal multimodal deep learning for action recognition—0
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification—0
Gameplay Highlights Generation—0
A two-way translation system of Chinese sign language based on computer vision—0
Motion-Augmented Self-Training for Video Recognition at Smaller Scale—0
Condensing a Sequence to One Informative Frame for Video Recognition—0
Action Detail Matters: Refining Video Recognition with Local Action Queries—0
Modeling Sub-Event Dynamics in First-Person Action Recognition—0
FlowGraph2Text: Automatic Sentence Skeleton Compilation for Procedural Text Generation—0
Flow-Distilled IP Two-Stream Networks for Compressed Video Action Recognition—0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
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