开放词汇伪装物体分割与级联视觉语言模型
Open-Vocabulary Camouflaged Object Segmentation with Cascaded Vision Language Models
AI总结：
本文提出了一种基于视觉语言模型的级联框架，用于解决开放词汇伪装物体分割中的领域差距和分割精度问题，通过共享VLM实现高效且语义一致的分割与分类。
摘要：
开放词汇伪装物体分割（OVCOS）旨在从任意类别中分割和分类伪装物体，由于视觉模糊性和未见类别，这带来了独特的挑战。最近的方法通常采用两阶段范式：首先分割物体，然后使用视觉语言模型（VLMs）对分割区域进行分类。然而，这些方法（1）由于VLMs全图像训练与裁剪区域推断之间的不匹配而产生领域差距，（2）依赖于针对明确界定物体优化的通用分割模型，使其在伪装物体上效果较差。在没有明确指导的情况下，通用分割模型往往忽视细微边界，导致分割不精确。在本文中，我们介绍了一种新的VLM引导的级联框架，以解决OVCOS中的这些问题。在分割方面，我们利用分割任何模型（SAM），由VLM引导。我们的框架使用VLM导出的特征作为显式提示给SAM，有效引导注意力集中在伪装区域，并显著提高定位精度。在分类方面，我们避免了硬裁剪引入的领域差距。相反，我们将分割输出作为alpha通道的软空间先验，保留完整图像上下文，同时提供精确的空间指导，从而实现更准确且上下文感知的伪装物体分类。相同的VLM在分割和分类之间共享，以确保效率和语义一致性。在OVCOS和传统伪装物体分割基准上的广泛实验表明，我们的方法具有明显优势，突显了利用丰富VLM语义进行伪装物体分割和分类的有效性。
英文摘要：
Open-Vocabulary Camouflaged Object Segmentation (OVCOS) seeks to segment and classify camouflaged objects from arbitrary categories, presenting unique challenges due to visual ambiguity and unseen categories.Recent approaches typically adopt a two-stage paradigm: first segmenting objects, then classifying the segmented regions using Vision Language Models (VLMs).However, these methods (1) suffer from a domain gap caused by the mismatch between VLMs' full-image training and cropped-region inference, and (2) depend on generic segmentation models optimized for well-delineated objects, making them less effective for camouflaged objects.Without explicit guidance, generic segmentation models often overlook subtle boundaries, leading to imprecise segmentation.In this paper,we introduce a novel VLM-guided cascaded framework to address these issues in OVCOS.For segmentation, we leverage the Segment Anything Model (SAM), guided by the VLM.Our framework uses VLM-derived features as explicit prompts to SAM, effectively directing attention to camouflaged regions and significantly improving localization accuracy.For classification, we avoid the domain gap introduced by hard cropping.Instead, we treat the segmentation output as a soft spatial prior via the alpha channel, which retains the full image context while providing precise spatial guidance, leading to more accurate and context-aware classification of camouflaged objects.The same VLM is shared across both segmentation and classification to ensure efficiency and semantic consistency.Extensive experiments on both OVCOS and conventional camouflaged object segmentation benchmarks demonstrate the clear superiority of our method, highlighting the effectiveness of leveraging rich VLM semantics for both segmentation and classification of camouflaged objects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
