The paper introduces D3S2, a diffusion‑guided dataset distillation framework tailored for semantic segmentation. It tackles long‑tailed class imbalance, pixel‑wise alignment, and high computational cost by first selecting class‑balanced masks and then synthesizing images with a pretrained diffusion model conditioned on those masks. Guided diffusion sampling further refines the data with segmentation‑consistency and class‑wise feature matching losses, achieving superior performance at a 1% compression rate on ADE20K and COCO‑Stuff.
By Wenjie Zheng, Haoji Hu, Jiali Lu, Xingze Zou, Jing Wang
arXiv:2608. 03218v1 Announce Type: cross Abstract: Dataset distillation compresses a large training set into a compact synthetic set while retaining its downstream utility.
By Mingzhuo Li, Guang Li, Linfeng Ye, Jiafeng Mao, Takahiro Ogawa, Konstantinos N. Plataniotis, Miki Haseyama
arXiv:2411.05005v2 Announce Type: replace-cross
Abstract: Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, m...
By Shuhong Zheng, Zhipeng Bao, Ruoyu Zhao, Martial Hebert, Yu-Xiong Wang
arXiv:2610.29361v1 Announce Type: new
Abstract: Infrared small target detection (IRSTD) is a challenging task due to the extremely small size of targets, low signal-to-noise ratios, and heavy backgro...
By Xuelin Qian, Zixuan Wang, Chang Xu, Dingwen Zhang, Junwei Han
The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy.
arXiv:2511.18031v1 Announce Type: cross
Abstract: Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that i...
By Yanxing Liu, Jiancheng Pan, Jianwei Yang, Tiancheng Chen, Peiling Zhou, Bingchen Zhang
Dataset distillation compresses a large training set into a compact synthetic set while retaining its downstream utility. Most existing methods target randomly initialized networks, whereas modern vision systems often adapt frozen pretrained encoders with lightweight modules.
arXiv:2607. 05319v1 Announce Type: cross Abstract: We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures.
By Rajat Rasal, Avinash Kori, Tian Xia, Ben Glocker
arXiv:2505.15147v3 Announce Type: replace
Abstract: Remote sensing images (RSIs) capture both natural and human-induced changes on the Earth's surface. Semantic segmentation (SS) of RSIs enables the...
By Quanwei Liu, Tao Huang, Jiaqi Yang, Wei Xiang
arXiv:2610.08539v1 Announce Type: new
Abstract: Remote sensing scene classification is a fundamental task in Earth observation and geospatial analysis. Existing approaches mainly follow three paradig...
By Dongchen Si, Di Wang, Mingzhen Xu, Jing Zhang, Bo Du, Liangpei Zhang
Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation can extend existing datasets with realistic images, and the quality of these images is generally assessed through fidelity metrics such as FID, KID, IS, LPIPS and SSIM that measure structural or distributional similarity.
arXiv:2507.21816v1 Announce Type: cross
Abstract: Few-shot object detection (FSOD) for optical remote sensing images aims to detect rare objects with only a few annotated bounding boxes. The limited...
By Yanxing Liu, Jiancheng Pan, Bingchen Zhang