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20232026
most citedDocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding

7 citations · 17 across the 64 of their papers we have counts for

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12 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.AI2026

Self-supervised Hierarchical Visual Reasoning with World Model

Yuanfei Xu, Lin Liu, Wengang Zhou +2

3D open-world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reasoning representations are ess…

cs.CV2026

Edit-GRPO: A Locality-Preserving Policy Optimization Framework for Image Editing

Shaodong Xu, Zexian Li, Zhendong Wang +5

A fundamental challenge in image editing lies in preserving spatial locality: edits should improve targeted content without inadvertently altering surrounding regions. However, mos…

cs.CV2026

Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers

Shaodong Xu, Zhendong Wang, Litong Gong +4

Recent advances in Diffusion Transformers (DiTs) demonstrate that aligning noisy latent states with well-trained semantic features-as pioneered by Representation Alignment (REPA)-c…

cs.AI2026

SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval

Xin Xie, Dongyun Xue, Wuguannan Yao +5

LLM-powered systems require complex multi-step decision-making abilities to solve real-world tasks, yet current planning approaches face a trade-off between the high latency of inf…

cs.RO2026

Structural Action Transformer for 3D Dexterous Manipulation

Xiaohan Lei, Min Wang, Bohong Weng +2

Achieving human-level dexterity in robots via imitation learning from heterogeneous datasets is hindered by the challenge of cross-embodiment skill transfer, particularly for high-…

cs.RO2026

Learning Surgical Robotic Manipulation with 3D Spatial Priors

Yu Sheng, Lidian Wang, Xiaomeng Chu +6

Achieving 3D spatial awareness is crucial for surgical robotic manipulation, where precise and delicate operations are required. Existing methods either explicitly reconstruct the…

cs.CV2026

StepVAR: Structure-Texture Guided Pruning for Visual Autoregressive Models

Keli Liu, Zhendong Wang, Wengang Zhou +1

Visual AutoRegressive (VAR) models based on next-scale prediction enable efficient hierarchical generation, yet the inference cost grows quadratically at high resolutions. We obser…

cs.RO2026

Primary-Fine Decoupling for Action Generation in Robotic Imitation

Xiaohan Lei, Min Wang, Wengang Zhou +2

Multi-modal distribution in robotic manipulation action sequences poses critical challenges for imitation learning. To this end, existing approaches often model the action space as…

cs.SE2026

CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation

Jianfeng Cai, Jinhua Zhu, Ruopei Sun +5

The rise of reasoning models necessitates large-scale verifiable data, for which programming tasks serve as an ideal source. However, while competitive programming platforms provid…