论文标题
主题FM：强大且可解释的主题辅助功能匹配
TopicFM: Robust and Interpretable Topic-Assisted Feature Matching
论文作者
论文摘要
这项研究解决了在具有挑战性的情况下的图像匹配问题，例如大型场景变化或无纹理场景。为了获得这种情况的鲁棒性，大多数以前的研究都试图通过图神经网络或变形金刚编码场景的全局上下文。但是，这些上下文并未明确表示高级上下文信息，例如结构形状或语义实例。因此，在具有挑战性的场景中，编码的功能仍然没有足够的歧视性。我们提出了一种新颖的图像匹配方法，该方法将主题模型策略应用于图像中的高级上下文。所提出的方法训练名为主题的潜在语义实例。它明确将图像模拟为主题的多项式分布，然后执行概率特征匹配。这种方法通过关注图像之间的相同语义区域来提高匹配的鲁棒性。此外，推断的主题为匹配结果提供了解释性，从而可以解释我们的方法。关于室外和室内数据集的广泛实验表明，我们的方法的表现优于其他最先进的方法，尤其是在具有挑战性的情况下。该代码可在https://github.com/truongkhang/topicfm上找到。
This study addresses an image-matching problem in challenging cases, such as large scene variations or textureless scenes. To gain robustness to such situations, most previous studies have attempted to encode the global contexts of a scene via graph neural networks or transformers. However, these contexts do not explicitly represent high-level contextual information, such as structural shapes or semantic instances; therefore, the encoded features are still not sufficiently discriminative in challenging scenes. We propose a novel image-matching method that applies a topic-modeling strategy to encode high-level contexts in images. The proposed method trains latent semantic instances called topics. It explicitly models an image as a multinomial distribution of topics, and then performs probabilistic feature matching. This approach improves the robustness of matching by focusing on the same semantic areas between the images. In addition, the inferred topics provide interpretability for matching the results, making our method explainable. Extensive experiments on outdoor and indoor datasets show that our method outperforms other state-of-the-art methods, particularly in challenging cases. The code is available at https://github.com/TruongKhang/TopicFM.
