https://github.com/ziyuzhao-zzy/logostylefool
공식 코드LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer
Papers with Code 아카이브
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/zmhh-h/mote
공식 코드MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet3d
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tsm
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tsm
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device
https://github.com/2023-MindSpore-4/Code11/tree/main/slowfast
https://github.com/2023-MindSpore-4/Code7/tree/main/slowfast
https://github.com/3D-ResNets-Reproduction/Reference-Project
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/3dperceptionlab/visual-wetlandbirds
https://github.com/AnjieZheng/OctaveConv-Pytorch
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/Atze00/MoViNet-pytorch
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/BestJuly/IIC
Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
https://github.com/Chianugoogidi/X3D-tf
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/DJAlexJ/LRCN-for-Video-Regression
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/Deepu1992/VideoClassification
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/EdoWhite/Gate-Shift-Pose
https://github.com/EquipoVandV/mmactionVandV
https://github.com/FenHua/SVA
https://github.com/HJYao00/Side4Video
What Can Simple Arithmetic Operations Do for Temporal Modeling?
https://github.com/IBM/AdaMML
AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition
https://github.com/JunweiLiang/aicity_action
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/KevinPang180/3D-ResNets-PaddlePaddle
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/MCG-NJU/CPD-Video
Omni-sourced Webly-supervised Learning for Video Recognition
https://github.com/MCG-NJU/OCSampler
OCSampler: Compressing Videos to One Clip with Single-step Sampling
https://github.com/MS-P3/code4/tree/main/SqueezeTime
No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding
https://github.com/MalongTech/research-v4d
V4D:4D Convolutional Neural Networks for Video-level Representation Learning
https://github.com/Manuscrit/OctaveConv_PlugAndPlay
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/Mind23-2/MindCode-68
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/Mind23-2/MindCode-88/tree/main/slowfast
https://github.com/MindCode-4/code-11/tree/main/drop-an-octave-reducing-spatial
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/MindCode-4/code-6/tree/main/drop-an-octave-reducing-spatial
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/MindSpore-paper-code-2/code2/tree/main/resnet3d
https://github.com/MindSpore-paper-code-2/code3/tree/main/tsm
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet3d
https://github.com/MindSpore-scientific-2/code-8/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-1/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.