https://github.com/nCheck/video-class-3d-resnet
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
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/nahidalam/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/najlaeLemrabet/FacialKeypointsDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/narumiruna/efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/nasib-ullah/video-captioning-models-in-Pytorch
https://github.com/nassimmokhtari/Enhanced-Spatio-Temporal-Image-Encoding
https://github.com/nassimmokhtari/Spatio-Temporal-Image-Encoding
https://github.com/natepuppy/HCN-pytorch
https://github.com/naver-ai/dawin
Leveraging Temporal Contextualization for Video Action Recognition
https://github.com/nddoshi/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/negarhdr/PGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/negarhdr/skeleton-based-action-recognition
https://github.com/neilsong/slt
https://github.com/nhannguyen2709/video_recognition
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/nieci2024/pyglcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/nifangbaage/explict-visual-prompt
Explicit Visual Prompting for Universal Foreground Segmentations
https://github.com/nikankind/Reproduce-Article-Representation-Flow-for-Action-Recognition-with-PaddlePaddle
https://github.com/nikitakaraevv/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/nimiew/Grab-Computer-Vision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/niveditarahurkar/CS231N-ActionRecognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/nkatzz/iled
Incremental Learning of Event Definitions with Inductive Logic Programming
https://github.com/nnaakkaaii/g2-MLP
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2
https://github.com/nokia-bell-labs/pretrained-imu-encoders
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text
https://github.com/nopperl/clip_arxiv_pmc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/northeastsquare/effficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/nuschandra/Tennis-Stroke-Detection
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/nzl-thu/musdl
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/oellop/GAN_MNIST
https://github.com/okankop/MFF-pytorch
https://github.com/oladeha2/shot_boudary_detector
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/olivesgatech/RoSE
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
https://github.com/opeco17/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/open-edge-platform/geti
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/open-edge-platform/training_extensions
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/open-mmlab/mmaction2
Learning Spatiotemporal Features with 3D Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.