463 citations · 558 across the 23 of their papers we have counts for
20 papers · 1 filter
LASER: A Corrective Lens for LVLMs via Visual Attention Preservation and Sink Suppression
Bowen Yuan, Zijian Wang, Yadan Luo +2
Large vision-language models (LVLMs) exhibit strong reasoning ability but suffer from visual forgetting during long-horizon decoding, where attention progressively drifts away from…
Learning to Align Generative Appearance Priors for Fine-grained Image Retrieval
Shijie Wang, Yadan Luo, Zijian Wang +2
Fine-grained image retrieval (FGIR) typically relies on supervision from seen categories to learn discriminative embeddings for retrieving unseen categories. However, such supervis…
Divide-and-Conquer Approach to Holistic Cognition in High-Similarity Contexts with Limited Data
Shijie Wang, Zijian Wang, Yadan Luo +3
Ultra-fine-grained visual categorization (Ultra-FGVC) aims to classify highly similar subcategories within fine-grained objects using limited training samples. However, holistic ye…
Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation
Shijie Wang, Zijian Wang, Yadan Luo +3
Wheat disease segmentation is fundamental to precision agriculture but faces severe challenges from significant intra-class temporal variations across growth stages. Such substanti…
Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data
Shijie Wang, Yadan Luo, Zijian Wang +3
This paper investigates the intrinsic geometrical features of highly similar objects and introduces a general self-supervised framework called the Geometric Attribute Exploration N…
Language-driven Fine-grained Retrieval
Shijie Wang, Xin Yu, Yadan Luo +3
Existing fine-grained image retrieval (FGIR) methods learn discriminative embeddings by adopting semantically sparse one-hot labels derived from category names as supervision. Whil…
Improving Out-of-Distribution Detection via Dynamic Covariance Calibration
Kaiyu Guo, Zijian Wang, Tan Pan +2
Out-of-Distribution (OOD) detection is essential for the trustworthiness of AI systems. Methods using prior information (i.e., subspace-based methods) have shown effective performa…
WisWheat: A Three-Tiered Vision-Language Dataset for Wheat Management
Bowen Yuan, Selena Song, Javier Fernandez +3
Wheat management strategies play a critical role in determining yield. Traditional management decisions often rely on labour-intensive expert inspections, which are expensive, subj…
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
Kaiyu Guo, Tan Pan, Chen Jiang +5
Medical anomaly detection (AD) is crucial for early clinical intervention, yet it faces challenges due to limited access to high-quality medical imaging data, caused by privacy con…
SCORE: Soft Label Compression-Centric Dataset Condensation via Coding Rate Optimization
Bowen Yuan, Yuxia Fu, Zijian Wang +2
Dataset Condensation (DC) aims to obtain a condensed dataset that allows models trained on the condensed dataset to achieve performance comparable to those trained on the full data…
Color-Oriented Redundancy Reduction in Dataset Distillation
Bowen Yuan, Zijian Wang, Mahsa Baktashmotlagh +2
Dataset Distillation (DD) is designed to generate condensed representations of extensive image datasets, enhancing training efficiency. Despite recent advances, there remains consi…
Learning Efficient Unsupervised Satellite Image-based Building Damage Detection
Yiyun Zhang, Zijian Wang, Yadan Luo +2
Existing Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions, hence largely limiting their application…
Open-CRB: Towards Open World Active Learning for 3D Object Detection
Zhuoxiao Chen, Yadan Luo, Zixin Wang +3
LiDAR-based 3D object detection has recently seen significant advancements through active learning (AL), attaining satisfactory performance by training on a small fraction of strat…
CIFAR-10-Warehouse: Broad and More Realistic Testbeds in Model Generalization Analysis
Xiaoxiao Sun, Xingjian Leng, Zijian Wang +3
Analyzing model performance in various unseen environments is a critical research problem in the machine learning community. To study this problem, it is important to construct a t…
Exploring Active 3D Object Detection from a Generalization Perspective
Yadan Luo, Zhuoxiao Chen, Zijian Wang +3
To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to an…
Source-Free Progressive Graph Learning for Open-Set Domain Adaptation
Yadan Luo, Zijian Wang, Zhuoxiao Chen +2
Open-set domain adaptation (OSDA) has gained considerable attention in many visual recognition tasks. However, most existing OSDA approaches are limited due to three main reasons,…
RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management
Zhuoxiao Chen, Yiyun Zhang, Yadan Luo +3
With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. Th…
Learning to Diversify for Single Domain Generalization
Zijian Wang, Yadan Luo, Ruihong Qiu +2
Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to…
Adversarial Bipartite Graph Learning for Video Domain Adaptation
Yadan Luo, Zi Huang, Zijian Wang +2
Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area due to the significant sp…
Progressive Graph Learning for Open-Set Domain Adaptation
Yadan Luo, Zijian Wang, Zi Huang +1
Domain shift is a fundamental problem in visual recognition which typically arises when the source and target data follow different distributions. The existing domain adaptation ap…