https://github.com/coderSkyChen/Action_Recognition_Zoo
Temporal Convolutional Networks for Action Segmentation and Detection
Papers with Code 아카이브
Action Detection
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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/colincsl/TemporalConvolutionalNetworks
Temporal Convolutional Networks for Action Segmentation and Detection
https://github.com/comp-well-org/Data_Augmentation_for_Biobehavioral_Time_Series_Data
Transformer Networks for Data Augmentation of Human Physical Activity Recognition
https://github.com/coolbay/VSGN
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/cybermonic/cage-4-submission
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/cyberpunk317/Action_detection
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
https://github.com/dairui01/MS-TCT
MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection
https://github.com/dairui01/PDAN
https://github.com/dairui01/TSU_evaluation
Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection
https://github.com/dais-ita/deepprobcep
https://github.com/dan-lennox/ml-udacity-quadcopter-rl
https://github.com/daniansan/i3d_mindspore
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/danielegrattarola/keras-gat
https://github.com/darkrush/meta_learn
https://github.com/darnbi/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/daveboat/structured_label_inference
https://github.com/davidpicard/homm
https://github.com/dchetelat/acer
https://github.com/debajyotid2/pointnet_pl
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/deepchem/moleculenet
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/denizmguen/IANNWTF2019-Project
https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum
https://github.com/dibschat/tempAgg
https://github.com/dlpbc/keras-kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/dmeoli/neuro-sat
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/dmlc/dgl/tree/master/examples/mxnet/sgc
https://github.com/dmlc/dgl/tree/master/examples/pytorch/arma
https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat
https://github.com/dmlc/dgl/tree/master/examples/pytorch/sgc
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/sgc
https://github.com/donshen/pointnet.phasedetection
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.