https://github.com/jacobkimmel/pytorch_convgru
Delving Deeper into Convolutional Networks for Learning Video Representations
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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.
Delving Deeper into Convolutional Networks for Learning Video Representations
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
VideoMix: Rethinking Data Augmentation for Video Classification
Sparse Autoencoders Find Highly Interpretable Features in Language Models
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
Two-Stream Convolutional Networks for Action Recognition in Videos
Is Space-Time Attention All You Need for Video Understanding?
Learning Transferable Visual Models From Natural Language Supervision
Semi-Supervised Classification with Graph Convolutional Networks
Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
Learning from Video and Text via Large-Scale Discriminative Clustering
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
A Closer Look at Spatiotemporal Convolutions for Action Recognition
A Closer Look at Spatiotemporal Convolutions for Action Recognition
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person Recognition
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
A Closer Look at Spatiotemporal Convolutions for Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.