85 citations · 118 across the 12 of their papers we have counts for
12 papers
BrowseComp-: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents
Huanyao Zhang, Jiepeng Zhou, Bo Li +22
Multimodal large language models (MLLMs), equipped with increasingly advanced planning and tool-use capabilities, are evolving into autonomous agents capable of performing multimod…
LGD: Leveraging Generative Descriptions for Zero-Shot Referring Image Segmentation
Jiachen Li, Qing Xie, Renshu Gu +3
Zero-shot referring image segmentation aims to locate and segment the target region based on a referring expression, with the primary challenge of aligning and matching semantics a…
Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning
Huajie Jiang, Zhengxian Li, Xiaohan Yu +4
Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes. It inevitably requires c…
Propensity-driven Uncertainty Learning for Sample Exploration in Source-Free Active Domain Adaptation
Zicheng Pan, Xiaohan Yu, Weichuan Zhang +1
Source-free active domain adaptation (SFADA) addresses the challenge of adapting a pre-trained model to new domains without access to source data while minimizing the need for targ…
EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse Principles
Zicheng Pan, Xiaohan Yu, Yongsheng Gao
Source-free domain adaptation (SFDA) aims to transfer knowledge from a labelled source domain to an unlabelled target domain. A major challenge in SFDA is deriving accurate categor…
SpectralKAN: Weighted Activation Distribution Kolmogorov-Arnold Network for Hyperspectral Image Change Detection
Yanheng Wang, Xiaohan Yu, Yongsheng Gao +6
Kolmogorov-Arnold networks (KANs) represent data features by learning the activation functions and demonstrate superior accuracy with fewer parameters, FLOPs, GPU memory usage (Mem…
A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization
Yajie Sun, Miaohua Zhang, Xiaohan Yu +2
Fine-grained visual categorization (FGVC), which aims at classifying objects with small inter-class variances, has been significantly advanced in recent years. However, ultra-fine-…
Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization
Zicheng Pan, Xiaohan Yu, Miaohua Zhang +1
While the fine-grained visual categorization (FGVC) problems have been greatly developed in the past years, the Ultra-fine-grained visual categorization (Ultra-FGVC) problems have…
PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refinement
Jun Wang, Hefeng Zhou, Xiaohan Yu
Current state-of-the-art weakly supervised object detection (WSOD) studies mainly follow a two-stage training strategy which integrates a fully supervised detector (FSD) with a pur…
Feature Fusion Vision Transformer for Fine-Grained Visual Categorization
Jun Wang, Xiaohan Yu, Yongsheng Gao
The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet discriminative features. Most previous works achieve this by explicitly selecting the dis…
EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
Jun Wang, Yang Zhao, Linglong Qian +2
The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e.g., diabetic, hypertensive and solar retinopathies. E…
Mask Guided Attention For Fine-Grained Patchy Image Classification
Jun Wang, Xiaohan Yu, Yongsheng Gao
In this work, we present a novel mask guided attention (MGA) method for fine-grained patchy image classification. The key challenge of fine-grained patchy image classification lies…