Discovering object-centric representations from images can significantly enhance the robustness, sample efficiency and generalizability of vision models. Works on images with multi-part objects typically follow an implicit object representation approach, which fail to recognize these learned objects in occluded or out-of-distribution contexts. This is due to the assumption that object part-whole relations are implicitly encoded into the representations through indirect training objectives. We address this limitation by proposing a novel method that leverages on explicit graph representations for parts and present a co-part object discovery algorithm. We then introduce three benchmarks to evaluate the robustness of object-centric methods in recognizing multi-part objects within occluded and out-of-distribution settings. Experimental results on simulated, realistic, and real-world images show marked improvements in the quality of discovered objects compared to state-of-the-art methods, as well as the accurate recognition of multi-part objects in occluded and out-of-distribution contexts. We also show that the discovered object-centric representations can more accurately predict key object properties in a downstream task, highlighting the potential of our method to advance the field of object-centric representations.

翻译：从图像中发现以物体为中心的表征能够显著提升视觉模型的鲁棒性、样本效率与泛化能力。针对包含多部件物体的图像，现有研究通常采用隐式物体表征方法，这类方法在遮挡或分布外场景下无法有效识别已学习的物体。其根本原因在于这些方法假设物体部件-整体关系通过间接的训练目标被隐式编码到表征中。为克服这一局限，我们提出一种新方法，该方法利用显式图结构表征部件，并提出一种协同部件物体发现算法。随后，我们引入三个基准测试，用于评估以物体为中心的方法在遮挡与分布外场景下识别多部件物体的鲁棒性。在模拟、拟真及真实世界图像上的实验结果表明，与现有先进方法相比，本方法在发现物体的质量上取得显著提升，并能在遮挡与分布外场景下准确识别多部件物体。我们还证明，所发现的以物体为中心的表征在下游任务中能更准确地预测关键物体属性，这凸显了本方法在推动以物体为中心的表征研究领域发展的潜力。