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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 151–200 of 307 papers

TitleStatusHype
Multi-Modal Multi-Action Video RecognitionCode0
Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a VideoCode0
Learning to Localize Temporal Events in Large-scale Video DataCode0
Collaborative Spatio-temporal Feature Learning for Video Action RecognitionCode0
Collaborative Spatiotemporal Feature Learning for Video Action RecognitionCode0
Inter-intra Variant Dual Representations forSelf-supervised Video RecognitionCode0
Flow-Guided Feature Aggregation for Video Object DetectionCode0
FAR: Fourier Aerial Video RecognitionCode0
Sparse Black-box Video Attack with Reinforcement LearningCode0
Spatial-temporal Concept based Explanation of 3D ConvNetsCode0
VTD-CLIP: Video-to-Text Discretization via Prompting CLIPCode0
Revisiting 3D ResNets for Video RecognitionCode0
Gate-Shift-Fuse for Video Action RecognitionCode0
ST-ABN: Visual Explanation Taking into Account Spatio-temporal Information for Video RecognitionCode0
HaltingVT: Adaptive Token Halting Transformer for Efficient Video RecognitionCode0
GenRec: Unifying Video Generation and Recognition with Diffusion ModelsCode0
PosMLP-Video: Spatial and Temporal Relative Position Encoding for Efficient Video RecognitionCode0
VideoPure: Diffusion-based Adversarial Purification for Video RecognitionCode0
MLP-3D: A MLP-like 3D Architecture with Grouped Time MixingCode0
Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition—0
On the Importance of Spatial Relations for Few-shot Action Recognition—0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition—0
PA3D: Pose-Action 3D Machine for Video Recognition—0
Towards Scalable Modeling of Compressed Videos for Efficient Action Recognition—0
Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos—0
Transfer Learning for Video Recognition with Scarce Training Data for Deep Convolutional Neural Network—0
Attention Distillation for Learning Video Representations—0
Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition—0
Phase-Specific Augmented Reality Guidance for Microscopic Cataract Surgery Using Long-Short Spatiotemporal Aggregation Transformer—0
Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios—0
Action Keypoint Network for Efficient Video Recognition—0
A^2-Nets: Double Attention Networks—0
Purification Of Contaminated Convolutional Neural Networks Via Robust Recovery: An Approach with Theoretical Guarantee in One-Hidden-Layer Case—0
PV-NAS: Practical Neural Architecture Search for Video Recognition—0
Action Detail Matters: Refining Video Recognition with Local Action Queries—0
A Simple and Efficient Baseline for Video Action Recognition—0
Attention Transfer from Web Images for Video Recognition—0
A two-way translation system of Chinese sign language based on computer vision—0
Recognizing Actions in Videos from Unseen Viewpoints—0
Audio-Visual Fusion Layers for Event Type Aware Video Recognition—0
Audio-Visual Glance Network for Efficient Video Recognition—0
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search—0
A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network—0
Recurrent Residual Module for Fast Inference in Videos—0
A robust and efficient video representation for action recognition—0
Black-box Adversarial Attacks on Video Recognition Models—0
Recurring the Transformer for Video Action Recognition—0
BosphorusSign22k Sign Language Recognition Dataset—0
11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks—0
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