无需标注数据，用文字描述就能准确定位图像中的物体。
Text-guided Zero-Shot Object Localization
- 利用CLIP融合图文信息，通过提示词引导定位
- 引入文本自相似匹配模块，提升定位准确率
- 适合无标注数据场景，为零样本定位提供新思路
目标定位是计算机视觉领域的热点问题，旨在从图像或视频中识别并确定特定物体的精确位置。现有方法大多依赖大量标注数据，而这些数据标注成本高，限制了其应用范围。为此，本文提出一种新的零样本目标定位（ZSOL）框架。该框架引入对比语言-图像预训练（CLIP）模块，有效整合视觉与语言信息；同时设计文本自相似匹配（TSSM）模块，增强由CLIP提取的文本特征表示，从而在无标注样本条件下，仅通过提示词即可实现对图像中特定物体的精准定位。大量实验结果表明，所提方法显著提升了定位性能，为后续研究建立了有效基准。
原文摘要 · Abstract (English)
Object localization is a hot issue in computer vision area, which aims to identify and determine the precise location of specific objects from image or video. Most existing object localization methods heavily rely on extensive labeled data, which are costly to annotate and constrain their applicability. Therefore, we propose a new Zero-Shot Object Localization (ZSOL) framework for addressing the aforementioned challenges. In the proposed framework, we introduce the Contrastive Language Image Pre-training (CLIP) module which could integrate visual and linguistic information effectively. Furthermore, we design a Text Self-Similarity Matching (TSSM) module, which could improve the localization accuracy by enhancing the representation of text features extracted by CLIP module. Hence, the proposed framework can be guided by prompt words to identify and locate specific objects in an image in the absence of labeled samples. The results of extensive experiments demonstrate that the proposed method could improve the localization performance significantly and establishes an effective benchmark for further research.
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