专题:Domain Adaptation and Few-Shot Learning

This cluster of papers focuses on the advances in transfer learning and domain adaptation, including topics such as few-shot learning, unsupervised learning, representation learning, deep networks, meta-learning, visual recognition, semi-supervised learning, and clustering analysis.
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Learning Multiple Layers of Features from Tiny Images

article Full Text OpenAlex 23383 FWCI559.593

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

preprint Full Text OpenAlex 14211 FWCI0

YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors

conference-paper Full Text OpenAlex 13153 FWCI1394.0826

Segment Anything

conference-paper Full Text OpenAlex 11820 FWCI1271.0531

Masked Autoencoders Are Scalable Vision Learners

conference-paper Full Text OpenAlex 8430 FWCI953.1759

A ConvNet for the 2020s

conference-paper Full Text OpenAlex 8393 FWCI567.699

Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation

conference-paper Full Text OpenAlex 5612 FWCI578.5824

DETRs Beat YOLOs on Real-time Object Detection

conference-paper Full Text OpenAlex 4889 FWCI1018.3015

YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

conference-paper Full Text OpenAlex 4779 FWCI997.0217

A Survey on Vision Transformer

article Full Text OpenAlex 4667 FWCI295.8874