https://github.com/MDSKUL/MasterProject
VideoBERT: A Joint Model for Video and Language Representation Learning
Papers with Code 아카이브
Action Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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Image source: The Kinetics Human Action Video Dataset
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MS-Mind/MS-Code-06/tree/main/Non_local
https://github.com/MS-Mind/MS-Code-08/tree/main/Non_local
https://github.com/MS-P3/code7/tree/main/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MS-P3/code7/tree/main/vivit
https://github.com/MahyarFardin/Monocular_depth_estimation_via_transfer_learning
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Manuscrit/OctaveConv_PlugAndPlay
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/MarkoLewis-Projects/Sign_language_detection
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Michaelgod/test
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/Mikkil5112/PredNet
https://github.com/Mind23-2/MindCode-101/tree/main/MVD
https://github.com/Mind23-2/MindCode-101/tree/main/Non_local
https://github.com/Mind23-2/MindCode-3/tree/main/I3D
https://github.com/Mind23-2/MindCode-3/tree/main/MVD
https://github.com/Mind23-2/MindCode-49
https://github.com/Mind23-2/MindCode-50
https://github.com/Mind23-2/MindCode-62
Video Classification with Channel-Separated Convolutional Networks
https://github.com/Mind23-2/MindCode-88/tree/main/slowfast
https://github.com/MindCode-4/code-1/tree/main/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
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-13/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindCode-4/code-3/tree/main/florence2
https://github.com/MindCode-4/code-5/tree/main/swin
https://github.com/MindCode-4/code-5/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/MindCode-4/code-5/tree/main/vit_mae
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/MindCode-4/code-9/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindSpore-paper-code-2/code2/tree/main/Non_local
https://github.com/MindSpore-paper-code-2/code2/tree/main/r2plus1d
Video Classification with Channel-Separated Convolutional Networks
https://github.com/MindSpore-paper-code-2/code2/tree/main/resnet3d
https://github.com/MindSpore-paper-code-2/code3/tree/main/TNT
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
https://github.com/MindSpore-paper-code-2/code3/tree/main/trn
https://github.com/MindSpore-paper-code-2/code3/tree/main/tsn
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet3d
https://github.com/MindSpore-paper-code-3/code8/tree/main/dpn
https://github.com/MindSpore-paper-code-3/code8/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.