VIPA: 视觉信息部分注意力用于指认图像分割
VIPA: Visual Informative Part Attention for Referring Image Segmentation
AI总结：
VIPA通过视觉信息部分注意力和视觉表达生成器提升指认图像分割的细粒度分割性能。
摘要：
指认图像分割（RIS）旨在根据自然语言表达对目标对象进行分割。现有方法通过将视觉信息融入语言标记来发展。为了更有效地利用视觉上下文进行细粒度分割，我们提出了一个新颖的视觉信息部分注意力（VIPA）框架用于指认图像分割。VIPA利用视觉上下文中的信息部分，称为视觉表达，可以有效地将结构和语义的视觉目标信息提供给网络。这种设计减少了高方差的跨模态投影，并在指认图像分割的注意力机制中增强了语义一致性。我们还设计了一个视觉表达生成器（VEG）模块，该模块通过局部-全局语言上下文提示检索信息性视觉标记，并通过减少噪声信息和共享信息性视觉属性来细化检索的标记。该模块使视觉表达能够考虑全面的上下文，并捕捉信息区域的语义视觉上下文。通过这种方式，我们的框架使网络的注意力能够稳健地对齐感兴趣的细粒度区域。广泛的实验和视觉分析证明了我们方法的有效性。我们的VIPA在四个公开的RIS基准上优于现有的最先进方法。
英文摘要：
Referring Image Segmentation (RIS) aims to segment a target object described by a natural language expression. Existing methods have evolved by leveraging the vision information into the language tokens. To more effectively exploit visual contexts for fine-grained segmentation, we propose a novel Visual Informative Part Attention (VIPA) framework for referring image segmentation. VIPA leverages the informative parts of visual contexts, called a visual expression, which can effectively provide the structural and semantic visual target information to the network. This design reduces high-variance cross-modal projection and enhances semantic consistency in an attention mechanism of the referring image segmentation. We also design a visual expression generator (VEG) module, which retrieves informative visual tokens via local-global linguistic context cues and refines the retrieved tokens for reducing noise information and sharing informative visual attributes. This module allows the visual expression to consider comprehensive contexts and capture semantic visual contexts of informative regions. In this way, our framework enables the network's attention to robustly align with the fine-grained regions of interest. Extensive experiments and visual analysis demonstrate the effectiveness of our approach. Our VIPA outperforms the existing state-of-the-art methods on four public RIS benchmarks.
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