注意力引导掩码是否真的有助于以对象为中心学习中的对象发现？

中文摘要 AI 辅助

对象中心学习（OCL）旨在无需人工标注的情况下将图像分解为对象。主流方法中的一个主要家族使用槽注意力将图像特征聚合为对象级表示，然后从中重建被掩码的图像内容，即随机掩码（RM），以提供自监督信号。近期方法DIAS仅以均匀随机性掩码图像块，却达到了有竞争力的对象发现准确率。由于聚合过程中的注意力已具备对象发现能力，我们探索利用它来开发更好的图像块掩码策略，即注意力引导掩码（AGM），从而提供更好的自监督信号。在六个公认数据集上的结果表明，AGM并不总是优于RM。在无条件槽初始化下，AGM在具有真实纹理的数据集（COCO和VOC）上显著改善了背景分割；无论条件或无条件槽初始化，且跨数据集，前景对象发现保持相当或下降。我们建议OCL社区的同行研究者，尝试利用内部注意力语义通过掩码解码改进OCL是有风险的。我们的源代码、模型检查点和评估日志将在接收后发布。

英文摘要

Object-Centric Learning (OCL) aims to decompose images into objects without human annotations. A major family of mainstream methods uses Slot Attention to aggregate image features into object-level representations and then from them reconstructs masked image content, i.e., Random Masking (RM), to provide self-supervision. The recent method DIAS simply masks image patches at uniform randomness yet achieves competitive object discovery accuracy. Since attention during aggregation already possesses object discovery ability, we explore using it to develop a better image patch masking strategy, i.e., Attention Guided Masking (AGM), thereby providing better self-supervision. Results on six recognized datasets show that AGM does not always outperform RM. Under unconditional slot initialization, AGM substantially improves background segmentation on datasets with realistic textures (COCO and VOC); Regardless of conditional or unconditional slot initialization and across datasets, foreground object discovery remains comparable or decreases. We suggest peer researchers in the OCL community that attempts to exploit internal attention semantics to improve OCL with masked decoding are risky. Our source code, model checkpoints and evaluation logs is available on https://github.com/und-entropy/Does-Attention-Guided-Masking-Really-Help-Object-Discovery-in-Object-Centric-Learning-.

发表机构

• Guilin University of Electronic Technology（桂林电子科技大学）
• Aalto University（阿尔托大学）
• University of Oulu（奥卢大学）

机构由 AI 辅助整理，请以论文原文为准。