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20242026
most citedPersonalized Large Vision-Language Models

1 citations · 1 across the 12 of their papers we have counts for

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Showing 2026 · cs.CVShow all

6 papers · 2 filters

cs.CV2026

Back2Struct: Making Structured Images Editable Again

Pengyu Yan, Yixin Wu, Yunjie Tian +1

Structured images, such as diagrams, charts, and flowcharts, are inherently symbolic and can be compactly represented in an editable format, yet in practice, they are often rendere…

cs.CV2026

TRACE: Evidence Grounding-Guided Multi-Video Event Understanding and Claim Generation

Pengyu Yan, Akhil Gorugantu, Mahesh Bhosale +3

Multi-video event understanding demands models that can locate and attribute query-relevant evidence scattered across long, heterogeneous video corpora. Existing large vision-langu…

cs.CV2026

Score-Control for Hallucination Reduction in Diffusion Models

Mahesh Bhosale, Naresh Kumar Devulapally, Abdul Wasi +3

Diffusion models have emerged as the backbone of modern generative AI, powering advances in vision, language, audio and other modalities. Despite their success, they suffer from ha…

cs.CV2026

CRAFT: Critic-Refined Adaptive Key-Frame Targeting for Multimodal Video Question Answering

Mahesh Bhosale, Abdul Wasi, Vishvesh Trivedi +3

Grounded multi-video question answering over real-world news events requires systems to surface query-relevant evidence across heterogeneous video archives while attributing every…

cs.CV2026

FairLLaVA: Fairness-Aware Parameter-Efficient Fine-Tuning for Large Vision-Language Assistants

Mahesh Bhosale, Abdul Wasi, Shantam Srivastava +5

While powerful in image-conditioned generation, multimodal large language models (MLLMs) can display uneven performance across demographic groups, highlighting fairness risks. In s…

cs.CV2026

Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation

Chao Wu, Mahesh Bhosale, Kangxian Xie +3

Ambiguous 3D medical image segmentation often involves boundaries where different expert delineations are non-identical yet clinically plausible. Modeling such inter-observer varia…