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arXiv 2025年

作者： Tong, Xi Luo, Xing Yang, Jiangxin Cao, Yanpeng Li, Xin State Key Laboratory of Fluid Power and Mechatronic Systems Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China Section of Visual Computing and Creative Media School of Performance Visualization and Fine Arts Texas A&M University College StationTX77843 United States

Multispectral imaging plays a critical role in a range of intelligent transportation applications, including advanced driver assistance systems (ADAS), traffic monitoring, and night vision. However, accurate visible and thermal (RGB-T) image registration poses a significant challenge due to the considerable modality differences. In this paper, we present a novel joint Self-Correlation and Cross-Correspondence Estimation Framework (SC3EF), leveraging both local representative features and global contextual cues to effectively generate RGB-T correspondences. For this purpose, we design a convolution-transformer-based pipeline to extract local representative features and encode global correlations of intra-modality for inter-modality correspondence estimation between unaligned visible and thermal images. After merging the local and global correspondence estimation results, we further employ a hierarchical optical flow estimation decoder to progressively refine the estimated dense correspondence maps. Extensive experiments demonstrate the effectiveness of our proposed method, outperforming the current state-of-the-art (SOTA) methods on representative RGB-T datasets. Furthermore, it also shows competitive generalization capabilities across challenging scenarios, including large parallax, severe occlusions, adverse weather, and other cross-modal datasets (e.g., RGB-N and RGB-D). Copyright © 2025, The Authors. All rights reserved.

关键词： Advanced driver assistance systems

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ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis

arXiv

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arXiv 2023年

作者： Tan, Hongchen Yin, Baocai Wei, Kun Liu, Xiuping Li, Xin Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing100124 China Section of Visual Computing and Creative Media School of Performance Visualization and Fine Arts Texas A&M University College StationTX77843 United States School of Mathematical Sciences Dalian University of Technology Dalian116024 China The School of Electronic Engineering Xidian University Xi'An710071 China

We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images without any auxiliary information. The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout visual Refinement (LVR) loss. The ALR module aligns the layout structure (which refers to locations of objects and background) of a synthesized image with that of its corresponding real image. In ALR module, we proposed an Adaptive Layout Refinement (ALR) loss to balance the matching of hard and easy features, for more efficient layout structure matching. Based on the refined layout structure, the LVR loss further refines the visual representation within the layout area. Experimental results on two widely-used datasets show that ALR-GAN performs competitively at the Text-to-Image generation task. © 2023, CC BY.

关键词： Generative adversarial networks

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