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

arXiv:2609.15187 · cs.CV · Submitted 2026-09-14 · Read on arXiv

cs.CV

Submitted: 2026-09-14

Updated: 2026-09-21

Code: https://github.com/Genera1Z/SmoothSA

Terminology

• Aggregation — 22× in this paper · explained in 1 episode
• Attention Entropy — 9× in this paper · explained in 2 episodes
• Random Masking — 8× in this paper
• Attention Guided Masking — 7× in this paper
• Object-Centric Learning — 6× in this paper
• Transformer — 5× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• International Conference on Computer Vision — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes

Sources

• Smoothing Slot Attention Iterations and Recurrences

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models