Recent advancements in large vision-language models enabled visual object detection in open-vocabulary scenarios, where object classes are defined in free-text formats during inference. In this paper, we aim to probe the state-of-the-art methods for open-vocabulary object detection to determine to what extent they understand fine-grained properties of objects and their parts. To this end, we introduce an evaluation protocol based on dynamic vocabulary generation to test whether models detect, discern, and assign the correct fine-grained description to objects in the presence of hard-negative classes. We contribute with a benchmark suite of increasing difficulty and probing different properties like color, pattern, and material. We further enhance our investigation by evaluating several state-of-the-art open-vocabulary object detectors using the proposed protocol and find that most existing solutions, which shine in standard open-vocabulary benchmarks, struggle to accurately capture and distinguish finer object details. We conclude the paper by highlighting the limitations of current methodologies and exploring promising research directions to overcome the discovered drawbacks. Data and code are available at https://github.com/lorebianchi98/FG-OVD.

我们在这篇论文中通过引入动态词汇生成的评估方案来探索现有开放词汇物体检测方法对物体的细粒度属性及其部分了解的程度，并通过对几种最先进的开放词汇物体检测器的评估，发现大多数现有方法难以准确捕捉和区分物体的细节，并总结了当前方法的局限性和有望克服这些缺点的研究方向。

细节决定成败：评估针对细节理解的开放词汇物体检测器