https://github.com/stevedepp/yolo4asl
TSM: Temporal Shift Module for Efficient Video Understanding
Papers with Code 아카이브
Action Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 0
- 보존된 평가 기록
- 0
Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/sunjungAn/Tensorflow2.x-CV
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/susomena/DeepSlowMotion
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/svdesai/heading-date-estimation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/swetak20/Deep_into_CNNs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/syyeung/frameglimpses
End-to-end Learning of Action Detection from Frame Glimpses in Videos
https://github.com/taishan1994/pytorch_gat
https://github.com/talsperre/INFER
https://github.com/taniokah/liarr2017
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/tatp22/pytorch-fast-GAT
https://github.com/technion-cs-nlp/llmsknow
Editing Implicit Assumptions in Text-to-Image Diffusion Models
https://github.com/tengdahan/temporalalignnet
https://github.com/tensorflow/models/tree/master/research/audioset
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tensorflow/models/tree/master/research/slim
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/thaitruongan/camera-surveillance-ai
https://github.com/thakurnupur/Sparse-Adversarial-Perturbations-PyTorch
https://github.com/thanhtrunghuynh93/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/thefonseca/predictive-coding
Moments in Time Dataset: one million videos for event understanding
https://github.com/thestephencasper/latent_adversarial_training
Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
https://github.com/thughost2/Padam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/timeseriesAI/tsai/blob/main/tsai/models/RNN_FCN.py
https://github.com/timothylimyl/PointNet-Pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/titu1994/LSTM-FCN
https://github.com/titu1994/MLSTM-FCN
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.