activity
20162026
most citedICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)

30 citations · 68 across the 25 of their papers we have counts for

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Showing 2026 · cs.CVShow all

6 papers · 2 filters

cs.CV2026

TryOnReward: Learning Foveated Consistency for Reinforcement Fine-Tuning of Virtual Try-On

Xueheng Li, Yong Liu, Xiaolong Fu +5

Virtual Try-On (VTON) aims to dress a person with the reference garment, producing visually reasonable results aligned with human preferences. Turning this preference-oriented goal…

cs.CV2026

Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On

Yong Liu, Xiaolong Fu, Zihang Xu +10

We present Oxygen-TryOn, a unified foundation model for any-item virtual try-on. Rather than repurposing a general-purpose image editor, Oxygen-TryOn is fashion-native, built for t…

cs.CV2026

GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network

Chengze Ye, Linda-Sophie Schneider, Yipeng Sun +1

This paper proposes a Gaussian-Based Shift-Variant filtered backprojection (FBP) neural network, which is designed for the efficient reconstruction of non-circular trajectory cone…

cs.CV2026

WING: A Window-Prior-Based Generative Network with Gated Inception for Cross-Modality CT Synthesis

Siyuan Mei, Yan Xia, Yipeng Sun +7

Generating CT volumes from MRI and CBCT can improve treatment planning in adaptive radiotherapy while avoiding additional radiation exposure. However, direct regression of CT inten…

cs.CV2026

Speech-Guided Multimodal Learning for Vocal Tract Segmentation in Real-Time MRI

Daiqi Liu, Lukas Mulzer, Md Hasan +11

Segmenting vocal tract articulators in real-time MRI (rtMRI) is a challenging dynamic image segmentation problem characterized by low contrast, rapid motion, and limited spatial re…

cs.CV2026

LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment: Methods and Results

Xin Li, Daoli Xu, Wei Luo +27

This paper reviews the LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment. This challenge aims to raise a new direction, i.e., how to evaluate the loss of sem…