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20242026
most citedSDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognition

2 citations · 6 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

TraceVision: Trajectory-Aware Vision-Language Model for Human-Like Spatial Understanding

Fan Yang, Shurong Zheng, Hongyin Zhao +5

Recent Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities in image understanding and natural language generation. However, current approaches focus predominan…

cs.CV2026

CREM: Compression-Driven Representation Enhancement for Multimodal Retrieval and Comprehension

Lihao Liu, Yan Wang, Biao Yang +10

Multimodal Large Language Models (MLLMs) have shown remarkable success in comprehension tasks such as visual description and visual question answering. However, their direct applic…

cs.CV2026

UniRef-Image-Edit: Towards Scalable and Consistent Multi-Reference Image Editing

Hongyang Wei, Bin Wen, Yancheng Long +22

We present UniRef-Image-Edit, a high-performance multi-modal generation system that unifies single-image editing and multi-image composition within a single framework. Existing dif…

cs.CV2025

MRD: Multi-resolution Retrieval-Detection Fusion for High-Resolution Image Understanding

Fan Yang, Xingping Dong, Xin Yu +3

Understanding high-resolution (HR) images remains a critical challenge for multimodal large language models (MLLMs). Recent approaches leverage vision-based retrieval-augmented gen…

cs.CV2025

FilmSceneDesigner: Chaining Set Design for Procedural Film Scene Generation

Zhifeng Xie, Keyi Zhang, Yiye Yan +4

Film set design plays a pivotal role in cinematic storytelling and shaping the visual atmosphere. However, the traditional process depends on expert-driven manual modeling, which i…

cs.CV2025

Logics-Parsing Technical Report

Xiangyang Chen, Shuzhao Li, Xiuwen Zhu +7

Recent advances in Large Vision-Language models (LVLM) have spurred significant progress in document parsing task. Compared to traditional pipeline-based methods, end-to-end paradi…