PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models
This paper proposes PerceptionDLM, a multimodal diffusion language model optimized for efficient parallel region perception. Leveraging the parallel decoding nature of diffusion language models, it introduces efficient prompting and structured attention masking to enable simultaneous perception of multiple masked regions, significantly improving inference efficiency. A new benchmark, ParaDLC-Bench, is constructed to evaluate parallelism in visual perception. Experiments show competitive performance with substantial speed improvements for multi-region tasks.
[2606.19534] PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models
[Submitted on 17 Jun 2026]
Title:PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models
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Abstract: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.
Comments: Code available at this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2606.19534 [cs.CV]
(or arXiv:2606.19534v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.19534
arXiv-issued DOI via DataCite (pending registration)
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From: Yueyi Sun [view email] [v1] Wed, 17 Jun 2026 19:27:55 UTC (12,947 KB)
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