Papers with Code 아카이브
Action Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/toinesayan/node-classification-and-label-dependencies
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tomar840/two-stream-fusion-for-action-recognition-in-videos
Convolutional Two-Stream Network Fusion for Video Action Recognition
https://github.com/totalgood/viddesc
https://github.com/totti0223/gradcamplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/towhee-io/towhee
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
https://github.com/towhee-io/towhee/tree/main/towhee/models/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/trevor-richardson/rnn_zoo
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/tristangomez44/metrics-saliency-maps
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/trzy/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tsenghungchen/SA-tensorflow
https://github.com/ttengwang/ESGN
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
https://github.com/tuananh0305/GCN_ANOMALY_DETECTION
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/txyugood/PaddleMVF
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
https://github.com/txyugood/PaddlePoseC3D
https://github.com/uclaml/Padam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/udaydutt12/Neural-Nets
https://github.com/udlf/wsef
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/umich-curly/3dmapping
https://github.com/urw7rs/torch_skeleton
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
https://github.com/utsawk/CarND-Traffic-Sign-Classifier-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/uvavision/Balanced-Datasets-Are-Not-Enough
https://github.com/v-iashin/video_features
A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer
https://github.com/v1an1/gradcamplusplus-cats-v-dogs
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/vailatuts/SSFG-regularization
https://github.com/varennes/deep-autoencoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vayuvegula/kerasModels
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vcowwy/paddle-grad-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/vendyv/CodeSpeedy_Internship
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vgangal101/VGG_models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/victespinoza/NeuralStyleTransfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/vineeths96/Gradient-Compression
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vineeths96/heterogeneous-systems
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vinits5/learning3d
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.