通过合并重叠槽位提升物体分解精度
When Slots Compete: Slot Merging in Object-Centric Learning
- 提出槽位合并机制，动态整合重叠的潜在槽位
- 在对象发现与分割任务中，掩码质量显著优于现有方法
- 无需额外参数，可直接嵌入主流训练流程
基于槽位的对象中心学习将图像表示为一组潜在槽位，由解码器将其组合成图像或特征。传统方法中槽位数量固定，仅进行优化，导致多个槽位竞争同一实体的重叠区域。本文提出槽位合并：一种轻量级、即插即用的操作，在训练中合并重叠槽位。通过软交并比（Soft-IoU）量化槽位注意力图的重叠程度，并采用重心更新方式合并选定槽对，保留梯度流。合并策略遵循固定规则，阈值由重叠统计推导，无需额外可学习模块。集成至DINOSAUR的特征重构框架后，该方法在对象分解与掩码质量上均有提升，优于其他自适应方法，在对象发现与分割基准测试中表现更优。
原文摘要 · Abstract (English)
Slot-based object-centric learning represents an image as a set of latent slots with a decoder that combines them into an image or features. The decoder specifies how slots are combined into an output, but the slot set is typically fixed: the number of slots is chosen upfront and slots are only refined. This can lead to multiple slots competing for overlapping regions of the same entity rather than focusing on distinct regions. We introduce slot merging: a drop-in, lightweight operation on the slot set that merges overlapping slots during training. We quantify overlap with a Soft-IoU score between slot-attention maps and combine selected pairs via a barycentric update that preserves gradient flow. Merging follows a fixed policy, with the decision threshold inferred from overlap statistics, requiring no additional learnable modules. Integrated into the established feature-reconstruction pipeline of DINOSAUR, the proposed method improves object factorization and mask quality, surpassing other adaptive methods in object discovery and segmentation benchmarks.
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