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20242026
most citedFreeze-in gravitational waves and dark matter in warm inflation

2 citations · 2 across the 8 of their papers we have counts for

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cs.CV2026

Prior-Anchored Debiasing for Long-Tailed Multi-Organ Pathology Report Generation

Feng Yang, Jie Liu, Yubo Pang +5

Automated pathology report generation from Whole Slide Images (WSIs) has attracted increasing attention in digital pathology. However, existing methods are predominantly developed…

cs.CV2026

Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs

Siyuan Xu, Yibing Liu, Peilin Chen +3

Multimodal large language models (MLLMs) have raised new privacy challenges. On the data side, user-provided inputs often include unpredictable sensitive information; while on the…

cs.CV2026

When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editing

Siyuan Xu, Yibing Liu, Peilin Chen +3

Multimodal Large Language Models (MLLMs) enable flexible instruction-driven image editing, but privacy risks arise when user images expose diverse and user-specific private content…

cs.CV2025

When Privacy Meets Recovery: The Overlooked Half of Surrogate-Driven Privacy Preservation for MLLM Editing

Siyuan Xu, Yibing Liu, Peilin Chen +3

Privacy leakage in Multimodal Large Language Models (MLLMs) has long been an intractable problem. Existing studies, though effectively obscure private information in MLLMs, often o…

cs.CV2025

VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Results

Hanwei Zhu, Haoning Wu, Zicheng Zhang +26

This paper presents a summary of the VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models (LMMs), hosted as part of the ICCV 2025 Workshop on Visual Quali…

cs.CV2025

RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement

Hao Luo, Baoliang Chen, Lingyu Zhu +2

Scene observation from multiple perspectives would bring a more comprehensive visual experience. However, in the context of acquiring multiple views in the dark, the highly correla…