https://github.com/amirdy/dog-breed-classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
VideoMix: Rethinking Data Augmentation for Video Classification
Audio-Visual Scene Analysis with Self-Supervised Multisensory Features
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
Learning from Video and Text via Large-Scale Discriminative Clustering
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
MotionSqueeze: Neural Motion Feature Learning for Video Understanding
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning Character-Agnostic Motion for Motion Retargeting in 2D
Learning Spatiotemporal Features with 3D Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Reconstructing undersampled photoacoustic microscopy images using deep learning
Learning Spatiotemporal Features with 3D Convolutional Networks
Learning Transferable Visual Models From Natural Language Supervision
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.