3 citations · 4 across the 14 of their papers we have counts for
14 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…
Bayesian Test-time Adaptation for Object Recognition and Detection with Vision-language Models
Lihua Zhou, Mao Ye, Shuaifeng Li +7
Vision-language models (VLMs) such as CLIP and Grounding DINO have achieved remarkable success in object recognition and detection. However, their performance often degrades under…
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…
Text-Driven Causal Representation Learning for Source-Free Domain Generalization
Lihua Zhou, Mao Ye, Nianxin Li +7
Deep learning often struggles when training and test data distributions differ. Traditional domain generalization (DG) tackles this by including data from multiple source domains,…
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…
Bayesian Test-Time Adaptation for Vision-Language Models
Lihua Zhou, Mao Ye, Shuaifeng Li +5
Test-time adaptation with pre-trained vision-language models, such as CLIP, aims to adapt the model to new, potentially out-of-distribution test data. Existing methods calculate th…
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…