Action Recognition
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mikecheninoulu/Emotional-gesture-papers
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild
https://github.com/mindspore-ai/models/blob/master/research/cv/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ai/models/blob/master/research/cv/glore_res
https://github.com/mindspore-ai/models/blob/master/research/cv/glore_res200
https://github.com/mindspore-ai/models/tree/master/official/cv/C3D/src
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/mindspore-ai/models/tree/master/official/gnn/gat
https://github.com/mindspore-ai/models/tree/master/research/cv/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/mindspore-ai/models/tree/master/research/cv/r2plus1d
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/mindspore-ai/models/tree/master/research/cv/stnet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/mindspore-ai/models/tree/master/research/cv/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/mindspore-ai/models/tree/master/research/cv/tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/MBConv.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mingxingtan/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/minhanh151/pre
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/minhanh151/respro
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/minhncedutw/pointnet1_keras
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/minostauros/VIAR
Unsupervised Learning of View-invariant Action Representations
https://github.com/miramind/efficientnets_pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mirthai/csa-net
https://github.com/mitya8128/experiments_notes
https://github.com/ml-jku/cloob
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlbio-epfl/turtle
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlfoundations/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlfoundations/open_flamingo
https://github.com/mlzxzhou/keras-gnm
https://github.com/mnikitin/EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/moabitcoin/ig65m-pytorch
Large-scale weakly-supervised pre-training for video action recognition
https://github.com/moein-shariatnia/OpenAI-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/morganmcg1/stanford-cars
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/morrisxu-driving/video-music_cross-modal_retrival
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mpuu00001/Siformer
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
https://github.com/mtanti/coco-caption
https://github.com/mtlouie-unm/alome-tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/musicalOffering/sola
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.