https://github.com/yfxc/tensorflow-T3D
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
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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.
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Is Space-Time Attention All You Need for Video Understanding?
Semi-Supervised Classification with Graph Convolutional Networks
Learning Transferable Visual Models From Natural Language Supervision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Learning Transferable Visual Models From Natural Language Supervision
Masked Feature Prediction for Self-Supervised Visual Pre-Training
Temporal Convolutional Networks for Action Segmentation and Detection
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning Transferable Visual Models From Natural Language Supervision
Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images
VideoLSTM Convolves, Attends and Flows for Action Recognition
Semi-Supervised Classification with Graph Convolutional Networks
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.