https://github.com/open-mmlab/mmclassification
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/open-mmlab/mmdetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/open-mmlab/mmflow
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/open-mmlab/mmpose
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
https://github.com/open-mmlab/mmsegmentation
https://github.com/open-mmlab/mmselfsup
Masked Feature Prediction for Self-Supervised Visual Pre-Training
https://github.com/open-mmlab/mmskeleton
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/openai/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/openmoss/language-model-saes
Sparse Autoencoders Find Highly Interpretable Features in Language Models
https://github.com/osmr/imgclsmob
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/otepencelik/GAN-Artwork-Generation
https://github.com/owena11/papers
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/paty0504/SIGNTEGRATE
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
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/peteanderson80/coco-caption
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/phixion/phixion
https://github.com/phoenix1327/tea-action-recognition
TEA: Temporal Excitation and Aggregation for Action Recognition
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/pikkaay/efficientnet_gpu
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/pminhtam/2D-3D_Multitask_Deep_Learning
2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
https://github.com/pmsharkKOR/Deep-Learning-Examples---Awesome-Resourse1
https://github.com/prabhupad26/100daysofML
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/prakhargoyal106/MelanomaClassification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.