引用关注位置：利用参考查询改进视觉定位
Referencing Where to Focus: Improving VisualGrounding with Referential Query
AI总结：
本文提出RefFormer，通过查询适配模块集成CLIP生成参考查询，为解码器提供目标相关先验，缓解学习难度并提升视觉定位性能，在五个基准上超越现有方法。
摘要：
视觉定位旨在给定自然语言表达的情况下，定位图像中的所指对象。近年来，基于DETR的视觉定位方法取得了显著进展，因为它们直接预测目标对象的坐标，而无需依赖额外的努力，例如预生成的候选提议或预定义的锚框。然而，现有研究主要聚焦于设计更强的多模态解码器，其通常通过随机初始化或使用语言嵌入来生成可学习查询。这种朴素的查询生成方式不可避免地增加了模型的学习难度，因为在解码开始时它不包含任何与目标相关的信息。此外，它们在查询学习过程中仅使用最深层的图像特征，忽视了其他层级特征的重要性。为了解决这些问题，我们提出了一种新颖的方法，称为RefFormer。它包含一个查询适配模块，可以无缝集成到CLIP中并生成参考查询，为解码器提供先验上下文，同时配备一个任务特定的解码器。通过将参考查询融入解码器，我们能够有效缓解解码器的学习难度，并准确地聚焦于目标对象。此外，我们提出的查询适配模块还可以充当适配器，保留CLIP中丰富的知识，而无需调整骨干网络的参数。大量实验证明了我们提出方法的有效性和效率，在五个视觉定位基准上超越了最先进的方法。
英文摘要：
Visual Grounding aims to localize the referring object in an image given a natural language expression. Recent advancements in DETR-based visual grounding methods have attracted considerable attention, as they directly predict the coordinates of the target object without relying on additional efforts, such as pre-generated proposal candidates or pre-defined anchor boxes. However, existing research primarily focuses on designing stronger multi-modal decoder, which typically generates learnable queries by random initialization or by using linguistic embeddings. This vanilla query generation approach inevitably increases the learning difficulty for the model, as it does not involve any target-related information at the beginning of decoding. Furthermore, they only use the deepest image feature during the query learning process, overlooking the importance of features from other levels. To address these issues, we propose a novel approach, called RefFormer. It consists of the query adaption module that can be seamlessly integrated into CLIP and generate the referential query to provide the prior context for decoder, along with a task-specific decoder. By incorporating the referential query into the decoder, we can effectively mitigate the learning difficulty of the decoder, and accurately concentrate on the target object. Additionally, our proposed query adaption module can also act as an adapter, preserving the rich knowledge within CLIP without the need to tune the parameters of the backbone network. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method, outperforming state-of-the-art approaches on five visual grounding benchmarks.
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。
