1 citations · 1 across the 8 of their papers we have counts for
11 papers
Neural-Collapse-guided Task-Free Continual Anomaly Detection
Xiaotong Kong, Chaoyang Song, Ziai Zhou +3
Recent years have witnessed growing interest in continual anomaly detection for industrial visual inspection. However, real-world manufacturing environments exhibit unpredictable s…
The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check
Qingyu Lu, Liang Ding, Kanjian Zhang +2
The pursuit of real-time agentic interaction has driven interest in Diffusion-based Large Language Models (dLLMs) as alternatives to auto-regressive backbones, promising to break t…
Classifier-Centric Adaptive Framework for Open-Vocabulary Camouflaged Object Segmentation
Hanyu Zhang, Yiming Zhou, Jinxia Zhang
Open-vocabulary camouflaged object segmentation requires models to segment camouflaged objects of arbitrary categories unseen during training, placing extremely high demands on gen…
Runaway is Ashamed, But Helpful: On the Early-Exit Behavior of Large Language Model-based Agents in Embodied Environments
Qingyu Lu, Liang Ding, Siyi Cao +4
Agents powered by large language models (LLMs) have demonstrated strong planning and decision-making capabilities in complex embodied environments. However, such agents often suffe…
MADiff: Text-Guided Fashion Image Editing with Mask Prediction and Attention-Enhanced Diffusion
Zechao Zhan, Dehong Gao, Jinxia Zhang +3
Text-guided image editing model has achieved great success in general domain. However, directly applying these models to the fashion domain may encounter two issues: (1) Inaccurate…
FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-Language Pre-training
Jiale Huang, Dehong Gao, Jinxia Zhang +3
Large-scale Vision-Language Pre-training (VLP) has demonstrated remarkable success in the general domain. However, in the fashion domain, items are distinguished by fine-grained at…
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models
Yeyuan Wang, Dehong Gao, Bin Li +7
The impressive performance of Large Language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks.…
Enhancing Fine-Grained Vision-Language Pretraining with Negative Augmented Samples
Yeyuan Wang, Dehong Gao, Lei Yi +4
Existing Vision-Language Pretraining (VLP) methods have achieved remarkable improvements across a variety of vision-language tasks, confirming their effectiveness in capturing coar…
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators
Qingyu Lu, Liang Ding, Kanjian Zhang +2
Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment, providing both scores and fine-grained feedback. Although a…
Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage
Yang Hu, Jinxia Zhang, Kaihua Zhang +4
Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial c…
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation
Jinxia Zhang, Xinyi Chen, Haikun Wei +1
Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectivene…