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20232026
most citedGenerating Valid and Natural Adversarial Examples with Large Language Models

1 citations · 1 across the 13 of their papers we have counts for

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cs.CV2026

Pareto-Guided Optimization for Uncertainty-Aware Medical Image Segmentation

Jinming Zhang, Youpeng Yang, Xi Yang +5

Uncertainty in medical image segmentation is inherently non-uniform, with boundary regions exhibiting substantially higher ambiguity than interior areas. Conventional training trea…

cs.CV2025

Hyperbolic Structured Classification for Robust Single Positive Multi-label Learning

Yiming Lin, Shang Wang, Junkai Zhou +3

Single Positive Multi-Label Learning (SPMLL) addresses the challenging scenario where each training sample is annotated with only one positive label despite potentially belonging t…

cs.CV2025

The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning

Yiming Lin, Yuchen Niu, Shang Wang +3

Context recognition (SR) is a fundamental task in computer vision that aims to extract structured semantic summaries from images by identifying key events and their associated enti…

cs.CV2025

Exploiting Layer Normalization Fine-tuning in Visual Transformer Foundation Models for Classification

Zhaorui Tan, Tan Pan, Kaizhu Huang +8

LayerNorm is pivotal in Vision Transformers (ViTs), yet its fine-tuning dynamics under data scarcity and domain shifts remain underexplored. This paper shows that shifts in LayerNo…

cs.CV2025

GeoSDF: Plane Geometry Diagram Synthesis via Signed Distance Field

Chengrui Zhang, Maizhen Ning, Tianyi Liu +4

Plane Geometry Diagram Synthesis has been a crucial task in computer graphics, with applications ranging from educational tools to AI-driven mathematical reasoning. Traditionally,…

cs.CV2025

DvD: Unleashing a Generative Paradigm for Document Dewarping via Coordinates-based Diffusion Model

Weiguang Zhang, Huangcheng Lu, Maizhen Ning +4

Document dewarping aims to rectify deformations in photographic document images, thus improving text readability, which has attracted much attention and made great progress, but it…