Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 4
- 보존된 평가 기록
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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/y2kmz/pointnetv2
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yCobanoglu/infinite-width-gnns
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yangbang18/video-classification-3d-cnn
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/yangjun1994/CAGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yangyucheng000/Paper-3/tree/main/opendmc
DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
https://github.com/yangyucheng000/glore_res200
https://github.com/yangyucheng000/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yangyucheng000/unipose-mindspore
UniPose: Unified Human Pose Estimation in Single Images and Videos
https://github.com/yanhuchen/quantum-graph-convolutional-network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yanx27/Pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yanxp/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yaohungt/GSTEG_CVPR_2019
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/yasar-rehman/fedvssl
https://github.com/ydtydr/hyla
https://github.com/ydzhang-stormstout/HAT
https://github.com/yevgm/deepadv3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yfsong0709/ResGCNv1
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
https://github.com/yfxc/tensorflow-T3D
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
https://github.com/yihengzhang-cv/mcl-motion-focused-contrastive-learning
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
https://github.com/yikangli/video-rhythm
https://github.com/yingsen1/unimd
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/yiskw713/ClassActivationMapping
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/yiskw713/SmoothGradCAMplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/yiskw713/scorecam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/yiyixuxu/TimeSformer-rolled-attention
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/yjxiong/action-detection
https://github.com/ykrmm/sota_gnn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yliess86/Bayeformers
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
https://github.com/ylqi/count-anything
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/youngjun0627/movie-rating
https://github.com/youngwoo-yoon/Practical-tips-for-lightweight-deep-learning
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ys-zong/medfair
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
https://github.com/ytng001/sensemaking
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ytw1996/swin3d_mindspore
https://github.com/yumaloop/LSTMAutoEncoderOnMovingMNIST
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.