https://github.com/mindspore-ai/models/tree/master/research/cv/tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
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/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/swin_transformer_v2.py
https://github.com/minyoungg/wmigftl
https://github.com/mirthai/csa-net
https://github.com/mlfoundations/open_clip
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/moabitcoin/ig65m-pytorch
https://github.com/modelscope/modelscope
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/mshilpaa/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/mtlouie-unm/alome-tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/mx-mark/dmjd
Masked Feature Prediction for Self-Supervised Visual Pre-Training
https://github.com/mx-mark/videotransformer-pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/mzolfaghari/chained-multistream-networks
https://github.com/nachiket273/pytorch_resnet_rs
Revisiting ResNets: Improved Training and Scaling Strategies
https://github.com/nhannguyen2709/video_recognition
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/nikankind/Reproduce-Article-Representation-Flow-for-Action-Recognition-with-PaddlePaddle
https://github.com/niveditarahurkar/CS231N-ActionRecognition
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/nku-shengzheliu/PaddlePaddle-Swin-Transformer-V2
https://github.com/nntanaka/Fourier-Analysis-for-Skeleton-based-Action-Recognition
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
https://github.com/nzl-thu/musdl
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/ojedaf/TNT
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
https://github.com/open-mmlab/mmaction2
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/open-mmlab/mmclassification
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/open-mmlab/mmselfsup
Masked Feature Prediction for Self-Supervised Visual Pre-Training
https://github.com/osmr/imgclsmob
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/owena11/papers
https://github.com/parkervg/allrecipes-bert
VideoBERT: A Joint Model for Video and Language Representation Learning
https://github.com/peachman05/action-recognition-tutorial
https://github.com/piergiaj/latent-subevents
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
https://github.com/piergiaj/pytorch-i3d
https://github.com/princeton-vl/d3dhelper
https://github.com/prinshul/GWSDR
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/pwc-1/Paper-10/tree/main/swin
https://github.com/pwc-1/Paper-10/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-9/tree/main/1/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-9/tree/main/1/vivit
https://github.com/pwc-1/Paper-9/tree/main/3/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.