1 citations · 3 across the 12 of their papers we have counts for
12 papers · 1 filter
HierDoc: Hierarchical Page-to-Region Evidence Routing for Long-Document Visual Question Answering
Rongjian Gu, Wengang Zhou, Junyu Xiong +4
Multi-page document visual question answering requires locating sparse evidence at both the page and region levels. Existing approaches typically emphasize one level over the other…
Hierarchical Evidence-Driven Reasoning for Long Document Understanding
Junyu Xiong, Yonghui Wang, Rongjian Gu +4
Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly curated subset. However, existi…
Revisiting Shadow Detection from a Vision-Language Perspective
Yonghui Wang, Shaokai Liu, Wengang Zhou +2
Shadow detection is commonly formulated as a vision-driven dense prediction problem, where models rely primarily on pixel-wise visual supervision to distinguish shadows from non-sh…
DocR1: Evidence Page-Guided GRPO for Multi-Page Document Understanding
Junyu Xiong, Yonghui Wang, Weichao Zhao +4
Understanding multi-page documents poses a significant challenge for multimodal large language models (MLLMs), as it requires fine-grained visual comprehension and multi-hop reason…
Text-to-3D Generation by 2D Editing
Haoran Li, Yuli Tian, Yonghui Wang +4
Distilling 3D representations from pretrained 2D diffusion models is essential for 3D creative applications across gaming, film, and interior design. Current SDS-based methods are…
ROOT: VLM based System for Indoor Scene Understanding and Beyond
Yonghui Wang, Shi-Yong Chen, Zhenxing Zhou +4
Recently, Vision Language Models (VLMs) have experienced significant advancements, yet these models still face challenges in spatial hierarchical reasoning within indoor scenes. In…
AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene Understanding
Yonghui Wang, Wengang Zhou, Hao Feng +1
Over the past few years, the advancement of Multimodal Large Language Models (MLLMs) has captured the wide interest of researchers, leading to numerous innovations to enhance MLLMs…
LaneTCA: Enhancing Video Lane Detection with Temporal Context Aggregation
Keyi Zhou, Li Li, Wengang Zhou +3
In video lane detection, there are rich temporal contexts among successive frames, which is under-explored in existing lane detectors. In this work, we propose LaneTCA to bridge th…
SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection
Yonghui Wang, Shaokai Liu, Li Li +2
Shadow detection is a fundamental and challenging task in many computer vision applications. Intuitively, most shadows come from the occlusion of light by the object itself, result…
TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding
Bozhi Luan, Hao Feng, Hong Chen +3
The advent of Large Multimodal Models (LMMs) has sparked a surge in research aimed at harnessing their remarkable reasoning abilities. However, for understanding text-rich images,…
Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs
Yonghui Wang, Wengang Zhou, Hao Feng +2
In the field of document understanding, significant advances have been made in the fine-tuning of Multimodal Large Language Models (MLLMs) with instruction-following data. Neverthe…
Progressive Recurrent Network for Shadow Removal
Yonghui Wang, Wengang Zhou, Hao Feng +2
Single-image shadow removal is a significant task that is still unresolved. Most existing deep learning-based approaches attempt to remove the shadow directly, which can not deal w…