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2026-03-10 01:21:06
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BO-QSA Improving Unsupervised Object-centric Learning with Query Optimization Baoxiong Jia* 1,3 , Yu Liu✶ 2,3 , Siyuan Huang 3 ✶ indicates equal contribution 1 UCLA 2 Tsinghua University 3 Beijing Institute for General Artificial Intelligence (BIGAI) Paper arXiv Code Abstract The ability to decompose complex natural scenes into meaningful object-centric abstractions lies at the core of human perception and reasoning. In the recent culmination of unsupervised object-centric learning, the Slot-Attention module has played an important role with its simple yet effective design and fostered many powerful variants. These methods, however, have been exceedingly difficult to train without supervision and are ambiguous in the notion of object, especially for complex natural scenes. In this paper, we propose to address these issues by improving previous attempts...
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