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Video Recognition
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Video Recognition is a process of obtaining, processing, and analysing data that it receives from a visual source, specifically video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/1adrianb/video-transformers
공식 코드https://github.com/BestJuly/Inter-intra-video-contrastive-learning
공식 코드Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
https://github.com/LukasHedegaard/co3d
공식 코드Continual 3D Convolutional Neural Networks for Real-time Processing of Videos
https://github.com/Luoyadan/MM2020_ABG
공식 코드Adversarial Bipartite Graph Learning for Video Domain Adaptation
https://github.com/MIT-HAN-LAB/temporal-shift-module
공식 코드TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/MedAI-Clemson/pda_detection
공식 코드On the Relevance of Temporal Features for Medical Ultrasound Video Recognition
https://github.com/ParitoshParmar/Piano-Skills-Assessment
공식 코드https://github.com/Qualcomm-AI-research/FrameExit
공식 코드FrameExit: Conditional Early Exiting for Efficient Video Recognition
https://github.com/RalphMao/PatchNet
공식 코드PatchNet -- Short-range Template Matching for Efficient Video Processing
https://github.com/ShihaoZhaoZSH/Video-Backdoor-Attack
공식 코드https://github.com/ShoufaChen/AdaptFormer
공식 코드AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
https://github.com/SmartEngines/stoppers_modelling
공식 코드https://github.com/SwinTransformer/Video-Swin-Transformer
공식 코드https://github.com/TheShadow29/VidSitu
공식 코드https://github.com/TrustAI/testRNN
공식 코드testRNN: Coverage-guided Testing on Recurrent Neural Networks
https://github.com/WISION-Lab/eventful-transformer
공식 코드Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers
https://github.com/ZilinGao/Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition
공식 코드Temporal-attentive Covariance Pooling Networks for Video Recognition
https://github.com/ailab-cvc/unireplknet
공식 코드Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations
https://github.com/alibaba-mmai-research/dist
공식 코드Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer Learning
https://github.com/alibaba-mmai-research/masked-action-recognition
공식 코드https://github.com/alibaba/lightweight-neural-architecture-search
공식 코드Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
https://github.com/anonymous-p/Flickering_Adversarial_Video
공식 코드Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks
https://github.com/baidu/Youtube-8M
공식 코드Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding
https://github.com/bespontaneous/afnet-pytorch
공식 코드https://github.com/bespontaneous/ffn
공식 코드https://github.com/bespontaneous/mca-pytorch
공식 코드Don't Judge by the Look: Towards Motion Coherent Video Representation
https://github.com/blackfeather-wang/AdaFocus
공식 코드https://github.com/blackfeather-wang/GFNet-Pytorch
공식 코드https://github.com/bomri/SlowFast/blob/master/projects/vtn/README.md
공식 코드https://github.com/brown-palm/ObjectPrompt
공식 코드Object-centric Video Representation for Long-term Action Anticipation
https://github.com/bruceyo/V-PETL
공식 코드Towards a Unified View on Visual Parameter-Efficient Transfer Learning
https://github.com/bytedance/Portrait-Mode-Video
공식 코드https://github.com/changsn/STViT-R
공식 코드Making Vision Transformers Efficient from A Token Sparsification View
https://github.com/chaoyuaw/lvu
공식 코드https://github.com/chenhaoxing/DeMamba
공식 코드DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.