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/labs12/Action-Recgontion-
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/lahaproject/inavit
Interaction Region Visual Transformer for Egocentric Action Anticipation
https://github.com/laplaisanterie/GAN
https://github.com/latte488/smth-smth-v2
The "something something" video database for learning and evaluating visual common sense
https://github.com/lauradhatt/Interesting-Reads
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
https://github.com/leftthomas/r2plus1d-c3d
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/leo-liuzy/codeupdatearena
CodeUpdateArena: Benchmarking Knowledge Editing on API Updates
https://github.com/leolee99/CLIP_ITM
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/hiera
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/lil-lab/chalet
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/lil-lab/ciff
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/lingzhang1/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/lingzhang1/pointnet_tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/linjieli222/hero_video_feature_extractor
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
https://github.com/lipingcoding/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/lisssse14/Grad_CAM_PLUS_PLUS
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/liu6zijian/simplified-gcn-model
https://github.com/liu824/representation-flow-for-action-recognition-paddlepaddle
https://github.com/liuch37/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/liujf69/TD-GCN-Gesture
https://github.com/liuruiyang98/Jittor-MLP
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
https://github.com/liyunqianggyn/Deep-Unsupervised-Image-Hashing
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/llm-editing/editing-attack
https://github.com/lmnt-com/haste
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/louisccc/av_av
https://github.com/louisccc/sg-risk-assessment
https://github.com/lpirola13/flower-recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lpirola13/flower_recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lucidrains/STAM-pytorch
https://github.com/lucidrains/TimeSformer-pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/lucidrains/flamingo-pytorch
https://github.com/lucidrains/magvit2-pytorch
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
https://github.com/lucidrains/uniformer-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.