3 citations · 4 across the 11 of their papers we have counts for
11 papers
Reward-Guided Semantic Evolution for Test-time Adaptive Object Detection
Lihua Zhou, Mao Ye, Xiatian Zhu +7
Open-vocabulary object detection with vision-language models (VLMs) such as Grounding DINO suffers from performance degradation under test-time distribution shifts, primarily due t…
FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection
Kaixiang Zhao, Mao Ye, Lihua Zhou +4
Open-vocabulary object detection often fails under distribution shifts, as it can be misled by spurious correlations between non-causal visual attributes (e.g., brightness, texture…
Source-Free Domain Adaptation with Vision-Language Prior
Song Tang, Yunxiang Bai, Wenxin Su +3
Source-Free Domain Adaptation (SFDA) seeks to adapt a source model, which is pre-trained on a supervised source domain, for a target domain, with only access to unlabeled target tr…
Consistent text-to-image generation via scene de-contextualization
Song Tang, Peihao Gong, Kunyu Li +5
Consistent text-to-image (T2I) generation seeks to produce identity-preserving images of the same subject across diverse scenes, yet it often fails due to a phenomenon called ident…
High Dynamic Range Novel View Synthesis with Single Exposure
Kaixuan Zhang, Hu Wang, Minxian Li +3
High Dynamic Range Novel View Synthesis (HDR-NVS) aims to establish a 3D scene HDR model from Low Dynamic Range (LDR) imagery. Typically, multiple-exposure LDR images are employed…
Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Data
Wenxin Su, Song Tang, Xiaofeng Liu +5
Domain shift (the difference between source and target domains) poses a significant challenge in clinical applications, e.g., Diabetic Retinopathy (DR) grading. Despite considering…
Source-Free Domain Adaptive Object Detection with Semantics Compensation
Song Tang, Jiuzheng Yang, Mao Ye +3
Strong data augmentation is a fundamental component of state-of-the-art mean teacher-based Source-Free domain adaptive Object Detection (SFOD) methods, enabling consistency-based s…
Few-Shot Medical Image Segmentation with High-Fidelity Prototypes
Song Tang, Shaxu Yan, Xiaozhi Qi +4
Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class. Despite the prototype based approac…
Proxy Denoising for Source-Free Domain Adaptation
Song Tang, Wenxin Su, Yan Gan +3
Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain with no access to the source data. Inspired by the success of large Visi…
Unified Source-Free Domain Adaptation
Song Tang, Wenxin Su, Mao Ye +2
In the pursuit of transferring a source model to a target domain without access to the source training data, Source-Free Domain Adaptation (SFDA) has been extensively explored acro…
Source-Free Domain Adaptation with Frozen Multimodal Foundation Model
Song Tang, Wenxin Su, Mao Ye +1
Source-Free Domain Adaptation (SFDA) aims to adapt a source model for a target domain, with only access to unlabeled target training data and the source model pre-trained on a supe…