no code implementations • 10 Jan 2024 • Nanqing Liu, Xun Xu, Yongyi Su, Chengxin Liu, Peiliang Gong, Heng-Chao Li
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather conditions.
1 code implementation • 6 Dec 2023 • Haojie Zhang, Yongyi Su, Xun Xu, Kui Jia
The success of large language models has inspired the computer vision community to explore image segmentation foundation model that is able to zero/few-shot generalize through prompt engineering.
1 code implementation • 26 Sep 2023 • Yongyi Su, Xun Xu, Kui Jia
Test-Time Adaptation aims to adapt source domain model to testing data at inference stage with success demonstrated in adapting to unseen corruptions.
1 code implementation • ICCV 2023 • Yushu Li, Xun Xu, Yongyi Su, Kui Jia
Existing approaches often focus on improving test-time training performance under well-curated target domain data.
no code implementations • 31 Mar 2023 • Yijin Chen, Xun Xu, Yongyi Su, Kui Jia
This motivates us to explore adapting an object detection model at test-time, a. k. a.
no code implementations • 20 Mar 2023 • Yongyi Su, Xun Xu, Tianrui Li, Kui Jia
Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required.
1 code implementation • 6 Jun 2022 • Yongyi Su, Xun Xu, Kui Jia
Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available and instant inference on target domain is required.
no code implementations • 6 May 2022 • Yongyi Su, Xun Xu, Kui Jia
Addressing the annotation challenge in 3D Point Cloud segmentation has inspired research into weakly supervised learning.