https://github.com/musikisomorphie/swd
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
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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.
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
Learning Transferable Visual Models From Natural Language Supervision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Masked Feature Prediction for Self-Supervised Visual Pre-Training
Is Space-Time Attention All You Need for Video Understanding?
Learning Spatiotemporal Features with 3D Convolutional Networks
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
Learning Transferable Visual Models From Natural Language Supervision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Leveraging Temporal Contextualization for Video Action Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Semi-Supervised Classification with Graph Convolutional Networks
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Semi-Supervised Classification with Graph Convolutional Networks
Explicit Visual Prompting for Universal Foreground Segmentations
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Semi-Supervised Classification with Graph Convolutional Networks
Learning Transferable Visual Models From Natural Language Supervision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.