arXiv:2608. 09482v1 Announce Type: cross Abstract: All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption using a single model.
By Chunxiao Liu, Wei Liu, Anbin Xiong, Erli Meng
arXiv:2609.13791v1 Announce Type: new
Abstract: Recognition pipelines typically adopt a restore-then-recognize workflow, yet decades of experience show that generating visually pleasing images seldom...
By Lanqing Guo, Xijun Wang, Minchul Kim, Yu Yuan, Wes Robbins, Xingguang Zhang, Nicholas Chimitt, Stanley H. Chan, Zhangyang Wang, Xiaoming Liu
The paper introduces RAE-CoD, a diffusion-based compression method that operates in a representation autoencoder space to preserve recognizable content even at extremely low bitrates. It addresses the problem of semantic collapse observed in existing codecs when the bitrate approaches zero, showing that reconstruction losses conflict with semantic objectives and that VAE diffusion models lose efficiency in preserving semantics. Experiments on MSCOCO-30K demonstrate that RAE-CoD outperforms competitors, reducing VFM feature MSE and Fréchet Distance ratios by at least 25.7% and 69.1% at 0.001–0.008 bpp while maintaining stable recognizability and quality.
By Tianyu Zhang, Zhaoyang Jia, Houqiang Li, Dong Liu
arXiv:2601. 12507v2 Announce Type: replace-cross Abstract: Low-resolution remote sensing small object detection is limited by both missing visual details and the ambiguity of how details serve detection.
By Ruo Qi, Linhui Dai, Yusong Qin, Chaolei Yang, Yanshan Li
arXiv:2609.01584v1 Announce Type: new
Abstract: Vehicle attribute analysis is a key component of Intelligent Transportation Systems (ITS), supporting applications such as vehicle identification, traf...
By Sergio M. Silva Jr., Otavio T. Remer, Gabriel E. Lima, Lucas Wojcik, Rayson Laroca, David Menotti
The paper introduces Restoring without Forgetting (RwF), a continual learning framework for image restoration that handles multiple degradations sequentially without accessing prior data. RwF trains a lightweight adapter for each new degradation, uses an unsupervised routing mechanism to select the correct restoration path, and achieves significant PSNR gains over fine‑tuning on five benchmark degradation domains. The method also demonstrates strong transfer performance on eleven real‑degradation datasets with high routing accuracy.
By Alif Ashrafee, Bartosz Krawczyk