https://github.com/thefonseca/predictive-coding
Moments in Time Dataset: one million videos for event understanding
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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.
Moments in Time Dataset: one million videos for event understanding
Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Temporal-Relational CrossTransformers for Few-Shot Action Recognition
Semi-Supervised Classification with Graph Convolutional Networks
Convolutional Two-Stream Network Fusion for Video Action Recognition
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
TSM: Temporal Shift Module for Efficient Video Understanding
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Semi-Supervised Classification with Graph Convolutional Networks
SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.