332 citations · 1.5k across the 59 of their papers we have counts for
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Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model
Hao Zhang, Lei Cao, Jiayi Ma
Existing multi-modal image fusion methods fail to address the compound degradations presented in source images, resulting in fusion images plagued by noise, color bias, improper ex…
Fusion from Decomposition: A Self-Supervised Approach for Image Fusion and Beyond
Pengwei Liang, Junjun Jiang, Qing Ma +2
Image fusion is famous as an alternative solution to generate one high-quality image from multiple images in addition to image restoration from a single degraded image. The essence…
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost
Yuan Gao, Weizhong Zhang, Wenhan Luo +4
We aim at exploiting additional auxiliary labels from an independent (auxiliary) task to boost the primary task performance which we focus on, while preserving a single task infere…
MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training
Jiayang Li, Junjun Jiang, Pengwei Liang +2
In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on trainin…
Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusion
Xunpeng Yi, Han Xu, Hao Zhang +2
Image fusion aims to combine information from different source images to create a comprehensively representative image. Existing fusion methods are typically helpless in dealing wi…
Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation
Linshan Wu, Zhun Zhong, Jiayi Ma +4
Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models by weak labels, which is receiving significant attention due to its low annotation cost. Existing a…
Latent Semantic Consensus For Deterministic Geometric Model Fitting
Guobao Xiao, Jun Yu, Jiayi Ma +2
Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality…