Abstract
Localization plays a crucial role in enhancing the practicality and precision of VQA systems. By enabling fine-grained identification and interaction with specific parts of an object, it significantly improves the system's ability to provide contextually relevant and spatially accurate responses, crucial for applications in dynamic environments like robotics and augmented reality. However, traditional systems face challenges in accurately mapping objects within images to generate nuanced and spatially aware responses. In this work, we introduce "Detect2Interact", which addresses these challenges by introducing an advanced approach for fine-grained object visual key field detection. First, we use the segment anything model (SAM) to generate detailed spatial maps of objects in images. Next, we use Vision Studio to extract semantic object descriptions. Third, we employ GPT-4's common sense knowledge, bridging the gap between an object's semantics and its spatial map. As a result, Detect2Interact achieves consistent qualitative results on object key field detection across extensive test cases and outperforms the existing VQA system with object detection by providing a more reasonable and finer visual representation.
Abstract (translated)
本地化在增强VQA系统的实用性和精确性方面发挥着关键作用。通过使系统能够精细地识别并交互特定物体部分，它显著提高了系统在动态环境（如机器人学和增强现实）中提供相关且准确响应的能力。然而，传统系统在准确地将图像中的物体映射到生成细微和空间感知响应方面面临挑战。在这项工作中，我们引入了“Detect2Interact”，通过引入一种高级的细粒度物体视觉关键词检测方法来解决这些挑战。首先，我们使用 segment anything model (SAM) 生成图像中物体的详细空间地图。接下来，我们使用 Vision Studio 提取语义物体描述。最后，我们利用 GPT-4 的常识知识，使物体语义和空间图之间建立联系。因此，Detect2Interact在广泛的测试用例中实现了对物体关键字段检测的一致质保，并超过了现有VQA系统。通过提供更加合理和精确的视觉表示，使其在动态环境中提供更加相关和准确的响应。
URL
https://arxiv.org/abs/2404.01151
