https://github.com/pwc-1/Paper-9/tree/main/4/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
Papers with Code 아카이브
Action Classification
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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/pwc-1/Paper-9/tree/main/5/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/pwc-1/Paper-9/tree/main/7/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/raajeshlr/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/rain58/boundary-aware-image-inpainting
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/rajatmodi62/occludedactionbenchmark
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/rami0205/ngramswin
https://github.com/ranjanguddu/DenseImageModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/ranjanguddu/DenseModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/renmada/OctConv-paddle
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/rijuldhir/TSM
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/rish-16/tokenlearner-pytorch
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/rishikksh20/ViViT-pytorch
https://github.com/rocksyne/kinetics-dataset-downloader
https://github.com/rohanshad/cmr_transformer
https://github.com/roshantac/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/rramjee/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/rsha0812/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/s-gupta/v-coco
https://github.com/salinasJJ/BBaction
Video Classification with Channel-Separated Convolutional Networks
https://github.com/satyajitghana/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/sebastiantiesmeyer/deeplabchop3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/seenu-g/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/seepala98/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/sense-x/uniformer
https://github.com/seominseok0429/inception-I3D-NON-LOCAL
https://github.com/shinya7y/UniverseNet
https://github.com/shkarupa-alex/tfswin
https://github.com/showmax/kinetics-downloader
https://github.com/siddinc/monocular_depth_estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/srilakshmiv14/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/stevedepp/yolo4asl
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/stunback/DenseDepth-paddle
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/sunutf/TSM
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/synce1234/FTGAN_custom
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
https://github.com/taoyang1122/adapt-image-models
AIM: Adapting Image Models for Efficient Video Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.