Papers with Code 아카이브
Action Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 8
- 평가표
- 11
- 보존된 평가 기록
- 81
Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tiago-roxo/bias
https://github.com/tianfr/mononerf
https://github.com/tianfr/semantic-flow
https://github.com/tilkb/thermoai
https://github.com/timothylimyl/PointNet-Pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/tk-rusch/graphcon
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/keras-gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tlmakinen/GAT-noise
https://github.com/toinesayan/node-classification-and-label-dependencies
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/toni-sm/skrl
https://github.com/tonysy/PyAction
https://github.com/towhee-io/towhee
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
https://github.com/toytiny/radarocc
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
https://github.com/trevor-richardson/rnn_zoo
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/tuananh0305/GCN_ANOMALY_DETECTION
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/txyugood/PaddlePoseC3D
https://github.com/udlf/wsef
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/urw7rs/torch_skeleton
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
https://github.com/vailatuts/SSFG-regularization
https://github.com/vijayvee/behavior-recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/vijayvee/behavior_recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/vinits5/learning3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/vnmr/JointVideoPose3D
Back to the Future: Joint Aware Temporal Deep Learning 3D Human Pose Estimation
https://github.com/vohoaiviet/activitynet-2016-cvprw
Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks
https://github.com/wan2000/mc-mot
https://github.com/wang-chen/correlation_flow
https://github.com/wangwen39/Temporal-Modeling-Methods-for-OAD
A Comprehensive Study on Temporal Modeling for Online Action Detection
https://github.com/wangxiang1230/SSTAP
https://github.com/wassname/rl-portfolio-management
https://github.com/weiyangfb/PyTorchSparseGAT
https://github.com/whut2962575697/gat_sementic_segmentation
https://github.com/witignite/Frustum-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wonderland-dsg/pointnet-grid
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wondonghyeon/protest-detection-violence-estimation
Protest Activity Detection and Perceived Violence Estimation from Social Media Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.