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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 76–100 of 307 papers

TitleStatusHype
Frame Flexible NetworkCode1
The effectiveness of MAE pre-pretraining for billion-scale pretrainingCode1
Efficient Decision-based Black-box Patch Attacks on Video Recognition—0
Video Action Recognition with Attentive Semantic Units—0
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language KnowledgeCode1
Making Vision Transformers Efficient from A Token Sparsification ViewCode1
MRET: Multi-resolution Transformer for Video Quality Assessment—0
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video RecognitionCode0
Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks—0
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight OptimizationCode1
Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge TransferringCode1
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on VideosCode0
Tiny Updater: Towards Efficient Neural Network-Driven Software UpdatingCode0
Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language ModelsCode2
Efficient Movie Scene Detection using State-Space TransformersCode1
Algorithm and Hardware Co-Design of Energy-Efficient LSTM Networks for Video Recognition with Hierarchical Tucker Tensor Decomposition—0
VLG: General Video Recognition with Web Textual KnowledgeCode1
SVFormer: Semi-supervised Video Transformer for Action RecognitionCode1
Look More but Care Less in Video RecognitionCode1
Temporal superimposed crossover module for effective continuous sign languageCode0
Cluster and Aggregate: Face Recognition with Large Probe SetCode1
Towards a Unified View on Visual Parameter-Efficient Transfer LearningCode1
REST: REtrieve & Self-Train for generative action recognition—0
AdaFocusV3: On Unified Spatial-temporal Dynamic Video RecognitionCode1
Rethinking Resolution in the Context of Efficient Video RecognitionCode1
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