Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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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/asif-salim/GCN-Graph_Convolutional_Network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/asiltureli/gan-in-colab
https://github.com/atmguille/Violence-Detection-With-Human-Skeletons
https://github.com/aurooj/Hand-Segmentation-in-the-Wild
https://github.com/aviros/pointnet_totations
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/aviros/roatationPointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/axd465/pam_dataset
Reconstructing undersampled photoacoustic microscopy images using deep learning
https://github.com/axon-research/c3d-keras
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/ayushraj7/Video-Anomaly-Detection
https://github.com/ayushrox/TSN_Colab
https://github.com/azshue/TPT
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/basiralab/RG-Select
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/Reproducible-Generative-Learning
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/reproduciblefedgnn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/baskargroup/Arboretum
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/baskargroup/biotrove
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/batzner/indrnn
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/bcsrn/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/bcv-uniandes/grasp
https://github.com/bcv-uniandes/matis
https://github.com/benedekrozemberczki/pytorch_geometric_temporal
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/bepping/non-oversmoothing-gcns
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/bespontaneous/ffn
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/bespontaneous/mca-pytorch
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/bespontaneous/proteus-pytorch
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/bestaar/predictiveFilterFlow
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
https://github.com/betterzh/sevlm-code
https://github.com/betterzh/sevlm-for-visual-art-comprehension
https://github.com/bharath3794/SceneSegmentation
A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
https://github.com/bhiziroglu/Conditional-Generative-Adversarial-Network
https://github.com/bidalab/improve
edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms
https://github.com/bionick87/ConvGRUCell-pytorch
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/bit-ml/dyreg-gnn
The "something something" video database for learning and evaluating visual common sense
https://github.com/blueberryc/pyGAT
https://github.com/booker-max/unsupervised-deraining-with-event-camera
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.