activity
20222024
most citedWhat Makes Good Examples for Visual In-Context Learning?

19 citations · 21 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models

Yabin Zhang, Wenjie Zhu, Hui Tang +3

With the emergence of pre-trained vision-language models like CLIP, how to adapt them to various downstream classification tasks has garnered significant attention in recent resear…

cs.CV2023

Semi-Supervised and Long-Tailed Object Detection with CascadeMatch

Yuhang Zang, Kaiyang Zhou, Chen Huang +1

This paper focuses on long-tailed object detection in the semi-supervised learning setting, which poses realistic challenges, but has rarely been studied in the literature. We prop…

cs.CV202319 cited

What Makes Good Examples for Visual In-Context Learning?

Yuanhan Zhang, Kaiyang Zhou, Ziwei Liu

Large-scale models trained on broad data have recently become the mainstream architecture in computer vision due to their strong generalization performance. In this paper, the main…

cs.CV20221 cited

On-Device Domain Generalization

Kaiyang Zhou, Yuanhan Zhang, Yuhang Zang +3

We present a systematic study of domain generalization (DG) for tiny neural networks. This problem is critical to on-device machine learning applications but has been overlooked in…

cs.CV2022

Panoptic Scene Graph Generation

Jingkang Yang, Yi Zhe Ang, Zujin Guo +3

Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i.e., objects are detected usin…

cs.CV20221 cited

Detecting Humans in RGB-D Data with CNNs

Kaiyang Zhou, Adeline Paiement, Majid Mirmehdi

We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two c…