AI看图不再排队：一次看懂多个区域
现在的AI看图说话，一次只能描述一个区域，要描述多个区域就得排队，效率低。这篇论文让AI能同时描述图片中的多个区域，就像人一眼扫过去能同时看到多个物体一样。他们用了一种叫“扩散语言模型”的技术，加上特殊的注意力掩码，让模型在生成文字时并行处理多个区域。在测试中，处理多个区域的速度比传统方法快了好几倍，而且描述质量不下降。虽然这项技术目前还停留在研究阶段，但它预示着未来的AI视觉助手能更快地理解复杂场景，比如同时识别照片里的所有人、物和文字。
📄 原文摘要(英文)
Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks. However, most existing MLLMs rely on autoregressive generation, which limits their efficiency for perception tasks that require captioning multiple regions. In this work, we propose PerceptionDLM, a multimodal diffusion language model optimized for efficient parallel region perception. Built upon PerceptionDLM-Base, a strong foundational baseline that achieves state-of-the-art performance among open-source diffusion MLLMs, our architecture fully leverages the parallel decoding nature of DLMs. Specifically, we introduce efficient prompting and structured attention masking to enable simultaneous perception of multiple masked regions, allowing the model to generate region descriptions in parallel at both the sequence and token levels. This design significantly improves inference efficiency compared with existing approaches that process regions sequentially. To systematically evaluate the parallelism property of visual perception capability for DLMs, we construct a new Parallel Detailed Localized Captioning Benchmark (ParaDLC-Bench) by scaling the DLC-Bench to include multiple region masks per image, enabling joint evaluation of both caption quality and inference efficiency. Experiments demonstrate that PerceptionDLM maintains competitive performance in region captioning while achieving substantial speed improvements for multi-region perception tasks. Our results highlight the potential of multimodal diffusion language models for efficient, parallel visual perception. To the best of our knowledge, we are the first to achieve parallel region caption and perception by leveraging the advantages of diffusion language models. Code, models, and datasets are released.