Does Attention-Guided Masking Really Help Object Discovery in Object-Centric Learning?

2026-09-14

AI Summary

研究探讨了在以物体为中心的学习中，注意力引导的遮罩（AGM）是否优于随机遮罩（RM），结果表明AGM仅在特定条件下改善背景分割。

Abstract

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 will be released upon acceptance.

Problem

• Object-Centric Learning
• Attention-Guided Masking
• Random Masking
• Foreground Object Discovery
• Background Segmentation

Innovation

• Attention Guided Masking
• Object-Centric Learning
• Slot Attention
• Random Masking
• Self-Supervision