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/kdha0727/lung-opacity-and-covid-chest-x-ray-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ken724049/action-recognition
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/kenshohara/video-classification-3d-cnn-pytorch
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/kenziyuliu/DGNN-PyTorch
https://github.com/keras-team/keras-io/blob/master/examples/vision/vivit.py
https://github.com/khu-vll/cast
CAST: Cross-Attention in Space and Time for Video Action Recognition
https://github.com/kietngt00/hmdb51-recognition
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/kiharalab/gnn_pocket
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/kingcong/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/kingcong/r2plus1d
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/kingcong/stnet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/kiyoon/pyvideoai
An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition
https://github.com/kjm1559/ADAE_LSTM_Autoencoder
https://github.com/kkahatapitiya/X3D-Multigrid
https://github.com/klemens-floege/oneprot
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/konstantinos-p/image_classification_SOTA
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/kshah33/viewcon
https://github.com/kylemin/S3D
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
https://github.com/kynkaat/role-of-imagenet-classes-in-fid
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/l13025816/PGCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/lRomul/ball-action-spotting
https://github.com/lab-midas/lapnet
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/labs12/Action-Recgontion-
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/lahaproject/inavit
Interaction Region Visual Transformer for Egocentric Action Anticipation
https://github.com/latte488/smth-smth-v2
The "something something" video database for learning and evaluating visual common sense
https://github.com/lauradhatt/Interesting-Reads
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
https://github.com/leftthomas/r2plus1d-c3d
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/leo-liuzy/codeupdatearena
CodeUpdateArena: Benchmarking Knowledge Editing on API Updates
https://github.com/leolee99/CLIP_ITM
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/hiera
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/lingzhang1/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/lingzhang1/pointnet_tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.