AI看图说话：一次说多个区域，还更快
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，慢。这篇论文让AI能同时描述图片里的多个区域，就像你一眼扫过去能同时看到好几个东西。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，描述多个区域的速度比传统方法快了好几倍，但描述质量没下降。这不是你明天就能用的功能，但它预示着未来AI看图会更像人——一眼看全，而不是逐行扫描。
📄 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.