https://github.com/shunk031/simple-aesthetics-predictor
Learning Transferable Visual Models From Natural Language Supervision
Papers with Code 아카이브
Activity Recognition
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- 평가표
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- 보존된 평가 기록
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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/shuuchen/video_autoencoder
https://github.com/sicara/tf-explain
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/sid-roheda/Volterra-Neural-Networks
https://github.com/simonmeister/UnFlow
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/sincerass/mvlpt
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sisl/event-driven-rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/sisl/gail-driver
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/sithu31296/simple-object-tracking
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/skamdar/gesture_recognition
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/skhetarpal/Prostate_Cancer_Grade_Assessment
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/small-whirlwind/EfficientGCN_paddle
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
https://github.com/smfirmin/DeepLearningFinalProject
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/snowkylin/gnn
https://github.com/snownus/COOP
https://github.com/soccernet/sn-grounding
https://github.com/song248/JOSENet_Fine-tuning
JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance Videos
https://github.com/soumik12345/point-cloud-segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/stanfordnlp/pyreft
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
https://github.com/stevedepp/yolo4asl
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/stillarrow/S2VT_ACT
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/stnoah1/infogcn
https://github.com/subercui/pyGConvAT
https://github.com/suhaslohit/TTN
Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping
https://github.com/sulasen/race-events-recognition-1
https://github.com/sunutf/TSM
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/sunxiaobei/mindspore_gallery/tree/main/grl/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/syyeung/frameglimpses
End-to-end Learning of Action Detection from Frame Glimpses in Videos
https://github.com/szq0214/CMC_with_Image_Mixture
https://github.com/szq0214/Rethinking-Image-Mixture-for-Unsupervised-Learning
https://github.com/taishan1994/pytorch_gat
https://github.com/taited/clip-score
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/takuhirok/rGAN
https://github.com/taolicheng/understanding-dnn
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
https://github.com/taoyang1122/adapt-image-models
AIM: Adapting Image Models for Efficient Video Action Recognition
https://github.com/tarikdgny/Optical_Coherence_Tomography
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.