Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/epic-kitchens/epic-kitchens-slowfast
https://github.com/epoc88/PFLD_68pts_Pytorch_2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/eps696/aphantasia
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/eric-mitchell/serac
https://github.com/ericksiavichay/cs230-final-project
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/ericyinyzy/vlattack
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/erktkdg/MTFL
https://github.com/eth212/Qmind_ModelNet10_PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ethanluoyc/transporter-pytorch
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/etna-team/etna
Time-Series Representation Learning via Temporal and Contextual Contrasting
https://github.com/event-ahu/safe_largevlm
https://github.com/fabiozappo/SkeletonGroupActivityRecognition
Learning Group Activities from Skeletons without Individual Action Labels
https://github.com/facebookresearch/ClassyVision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/facebookresearch/DomainBed
https://github.com/facebookresearch/Listen-to-Look
https://github.com/facebookresearch/brainmagick
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/facebookresearch/clip-rocket
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/facebookresearch/detectron
https://github.com/facebookresearch/hiervl
https://github.com/facebookresearch/pycls
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/facebookresearch/pytorchvideo
https://github.com/facebookresearch/xformers
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
https://github.com/fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition
https://github.com/fangwei123456/spikingjelly
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
https://github.com/fanzhenliu/dagad
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/farazahmeds/Classification-of-brain-tumor-using-Spatiotemporal-models
ConvNet Architecture Search for Spatiotemporal Feature Learning
https://github.com/fastscience-ai/medflamingo
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/fdsa1860/skeletal
https://github.com/federicopozzi33/MobileOne-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/fiabdu/Commonly-Interesting-Images
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/filaPro/visda2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/filipbasara0/simple-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/filipmu/Kaggle-APTOS-2019-Blindness
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/fingerrec/dsm-decoupling-scene-motion
Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion
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