https://github.com/pwc-1/Paper-9/tree/main/7/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
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/rajatmodi62/occludedactionbenchmark
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/renmada/OctConv-paddle
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/rijuldhir/TSM
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/rish-16/tokenlearner-pytorch
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/rlaengud123/CMC_LRCN
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/rohanshad/cmr_transformer
https://github.com/rutajoshi/cs231n-project
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/rwightman/pytorch-image-models
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/seujung/WeightStandardization_gluon
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
https://github.com/shelhamer/clockwork-fcn
https://github.com/stevedepp/yolo4asl
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/sujaygarlanka/tennis_stroke_recognition
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
https://github.com/sunutf/TSM
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/sunxm2357/VideoIQ
https://github.com/terrychenism/OctaveConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/tianfr/mononerf
https://github.com/tianfr/semantic-flow
https://github.com/tonysy/PyAction
https://github.com/towhee-io/towhee
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/towhee-io/towhee/tree/main/towhee/models/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/tuanzhangCS/octconv_resnet
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/txyugood/PaddleMVF
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
https://github.com/visaVita/mmaction2
https://github.com/vivym/OctaveConv.pytorch
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/w-sugar/Video-Swin-Transformer
https://github.com/wangxiang1230/SSTAP
https://github.com/wgcban/apt
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
https://github.com/whwu95/Cap4Video
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/whwu95/GPT4Vis
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/woshisad159/tfnet
Temporal superimposed crossover module for effective continuous sign language
https://github.com/xu3kev/octconv-chainer
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/youngjun0627/movie-rating
https://github.com/ytw1996/swin3d_mindspore
https://github.com/zhipeng-wei/Heuristic_black_box_adversarial_attack_on_video_recognition_models
Heuristic Black-box Adversarial Attacks on Video Recognition Models
https://github.com/ziqi-zhang/3D-ResNets-PyTorch
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.