arXiv:2608. 08135v1 Announce Type: cross Abstract: Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitations: methods operate on 2D slices or 3D patches rather than whole volumes, and train a separate model for each translation task.
By Daniele Molino, Alessio Zoboli, Camillo Maria Caruso, Valerio Guarrasi, Paolo Soda
arXiv:2608.22619v1 Announce Type: cross
Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-t...
By Md Maklachur Rahman, Md Hasan Al Banna, Saraf Anjum, Mahmudul Hasan, Tracy Hammond
MIAR introduces a multi‑scale autoregressive framework for medical image super‑resolution, treating the task as a conditional, progressive next‑scale prediction. It incorporates a Scale‑Adaptive Structural Decoder to preserve structural fidelity and uses a hierarchical beam search during inference to reduce recursive error accumulation. Experiments show MIAR outperforms existing methods, achieving a 7.86% MUSIQ improvement and a 2.02× speedup over diffusion‑based approaches.
By Fang Li, Yinglong Li, Hongyu Wu, Yang Gao, Minwei Zhao, Aimin Hao
The paper introduces MTAR, a training framework for autoregressive image generation that enhances performance through multi-token prediction, token-level contrastive regularization, and semantic dropping. These components address sparse supervision, improve representation discriminability, and accelerate training without affecting inference. On ImageNet, MTAR outperforms LlamaGen with lower FID and faster training, achieving comparable results in only a third of the iterations.
By Guo Niu, Xiongfei Yao, Teng Wang, Nannan Zhu
arXiv:2607. 00371v1 Announce Type: cross Abstract: Visual AutoRegressive modeling (VAR) has pioneered a coarse-to-fine multi-scale autoregressive generative paradigm, demonstrating strong capabilities in image generation.
By Nuoyan Zhou, Zhijun Tu, Lei Yu, Kun Cheng, Jie Hu, Nannan Wang, Xinghao Chen
ARGenSeg introduces an autoregressive generation-based approach for image segmentation that integrates seamlessly with multimodal large language models (MLLMs). Unlike prior methods that use boundary points or dedicated segmentation heads, ARGenSeg generates dense masks directly through visual token output and detokenization via a universal VQ‑VAE, enabling fine‑grained pixel‑level perception. The framework employs a next‑scale‑prediction strategy to parallelize token generation, resulting in faster inference while outperforming state‑of‑the‑art segmentation models on multiple datasets.
By Xiaolong Wang, Lixiang Ru, Ziyuan Huang, Kaixiang Ji, Dandan Zheng, Jingdong Chen, Jun Zhou