https://github.com/OpenXAIProject/LRP_for_LSTM_Korean_dataset
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 4
- 보존된 평가 기록
- 10
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/PIX2NVS/NVS_FeatureLearning
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
https://github.com/POLane16/Robust-Conditional-GAN
https://github.com/PPPrior/i3d-pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/PaParaZz1/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/PaParaZz1/TemporalShiftModule
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PASSL
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_CLIP_model.md
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/PaddlePaddle/PaddleClas
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/PaddlePaddle/PaddleDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/PaddlePaddle/PaddleSeg
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/recognition/videoswin.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/localization/bmn.md
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsn.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/slowfast.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/stgcn.md
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/timesformer.md
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/video/models/bsn
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/PaddlePaddle/models/tree/develop/dygraph/bmn
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/ParitoshParmar/C3D-LSTM--PyTorch
https://github.com/ParitoshParmar/HalluciNet--PyTorch
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/ParitoshParmar/LTSOE
https://github.com/Parth27/ActionRecognitionVideos
https://github.com/Pathakvishnu/Human-Activity-Recognition-
Human Activity Recognition with Convolutional Neural Netowrks
https://github.com/PaulEmmanuelSotir/DeepCV
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/Peihao-Xiang/Label-Ranker
https://github.com/PeterWang512/FALdetector
https://github.com/Phimos/Paddle-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/PotatoSpudowski/CactiNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Powercoder64/TAA-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/PumpkinYing/GAT
https://github.com/QUVA-Lab/timeception
https://github.com/RemyMess/MMVRC_ICCV_2021_Skeleton_based_Action_Recognition
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
https://github.com/RichardChangCA/CSI_5386_NLP_Project
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.