AI看图说话：一次看清多个区域，不再逐个排队
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处理，速度慢。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。他们通过设计特殊的注意力掩码和提示方式，让模型在生成文字时并行处理多个区域，速度提升明显，而且描述质量不下降。他们还建了一个新测试集来专门评估这种并行能力。虽然这还不是你明天就能用的产品，但它展示了AI视觉理解从“串行”走向“并行”的趋势，未来可能让实时图像描述、辅助视障人士等场景更高效。
📄 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.