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SlotLifter: Slot-guided Feature Lifting for Learning Object-centric Radiance Fields
Yu Liu*,
Baoxiong Jia*,
Yixin Chen,
Siyuan Huang,
ECCV 2024
[Paper]
 
[Project Page]
 
[Code (Coming soon)]
We propose SlotLifter, a novel object-centric radiance model that aims to address the challenges of scene reconstruction and decomposition via slot-guided feature lifting. Such a design unites object-centric learning representations and image-based rendering methods, offering state-of-the-art performance in scene decom-position and novel-view synthesis on four challenging synthetic and four complex real-world datasets, outperforming existing 3D object-centric learning methods by a large margin.
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Improving Object-centric Learning With Query Optimization
Baoxiong Jia*,
Yu Liu*,
Siyuan Huang,
ICLR 2023
[Paper]
 
[Project Page]
 
[Code]
We proposed methods for (1) initializing Slot-Attention modules with learnable queries and
(2) optimizing the model with bi-level optimization. With simple code adjustments on the
vanilla Slot-Attention, our model, Bi-level Optimized Query Slot Attention,
achieves state-of-the-art results on 3 challenging synthetic and 7 complex real-world datasets
in unsupervised image segmentation and reconstruction, outperforming previous baselines by a large margin.
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Beijing Institute for General Artificial Intelligence(BIGAI), China
2021.09 - now
Research Intern
Advisor: Dr. Baoxiong Jia
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Tsinghua University, China
2020.08 - 2024.07
Undergraduate Student
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Tsinghua University, China
2024.08 - now
Ph.D. candidate
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