理解Fisher向量：多模态零件模型
Understanding the Fisher Vector: a multimodal part model
摘要：
Fisher矢量和相关的无序视觉统计在目标检测中表现出优异的性能，有时优于已建立的方法，如可变形零件模型。然而，目前还不清楚这些模型如何使用有限大小的视觉码本和粗糙的几何信息来捕获复杂的外观变化。在这项工作中，我们建议将基于Fisher矢量的对象检测器解释为基于部分的模型。通过使用几个可视化和实验，我们表明，这是一个有用的洞察力来解释模型的良好性能。此外，我们首次揭示了几个有趣的属性的FV，包括它的工作能力，以及只使用一个小的输入补丁和视觉单词的子集。最后，我们讨论了FV探测器和TUV探测器的关系，指出了它们之间的区别和共同点。
英文摘要：
Fisher Vectors and related orderless visual statistics have demonstrated excellent performance in object detection, sometimes superior to established approaches such as the Deformable Part Models. However, it remains unclear how these models can capture complex appearance variations using visual codebooks of limited sizes and coarse geometric information. In this work, we propose to interpret Fisher-Vector-based object detectors as part-based models. Through the use of several visualizations and experiments, we show that this is a useful insight to explain the good performance of the model. Furthermore, we reveal for the first time several interesting properties of the FV, including its ability to work well using only a small subset of input patches and visual words. Finally, we discuss the relation of the FV and DPM detectors, pointing out differences and commonalities between them.
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。
