https://github.com/open-mmlab/mmselfsup
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
Papers with Code 아카이브
Temporal Action Localization
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Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/open-mmlab/mmskeleton
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/open3da/ll3da
https://github.com/openmeshlab/meshxl
https://github.com/openmoss/language-model-saes
Sparse Autoencoders Find Highly Interpretable Features in Language Models
https://github.com/osmr/imgclsmob
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/pablopardod/dyalyt
https://github.com/pairlab/v-cdn
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/pasrichashivam/Convnets_Grad-CAM_Grad-CAM-PlusPlus_Keras_tensorflow-2
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/pat-coady/tiny_imagenet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/patrick013/Image-Classification-CNN-and-VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/paty0504/SIGNTEGRATE
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/paulorauber/nn
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/pbaylies/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/pdemeulenaer/Time-series-prediction
https://github.com/peachman05/action-recognition-tutorial
https://github.com/pengsl-lab/mssl
https://github.com/pengsl-lab/mssl2drug
https://github.com/pengzhou1108/GSRNet
Generate, Segment and Refine: Towards Generic Manipulation Segmentation
https://github.com/pengzhou1108/RGB-N
https://github.com/peter-yys-yoon/pegcnv2
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
https://github.com/philferriere/tfoptflow
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/phiresky/pandoc-url2cite
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/phixion/phixion
https://github.com/piergiaj/latent-subevents
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
https://github.com/piergiaj/pytorch-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/pminhtam/2D-3D_Multitask_Deep_Learning
2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
https://github.com/pohlinwei/AComPianist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/pranav-chib/trajimpute
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
https://github.com/prasadsawant5/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/princeton-vl/SimpleView
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/princeton-vl/d3dhelper
https://github.com/prinshul/GWSDR
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/priyavrat-misra/xrays-and-gradcam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/pwc-1/Paper-9/tree/main/3/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/pwc-1/Paper-9/tree/main/3/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.