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- W4386075574 abstract "Object discovery – separating objects from the background without manual labels – is a fundamental open challenge in computer vision. Previous methods struggle to go beyond clustering of low-level cues, whether handcrafted (e.g., color, texture) or learned (e.g., from auto-encoders). In this work, we augment the auto-encoder representation learning framework with two key components: motion-guidance and mid-level feature tokenization. Although both have been separately investigated, we introduce a new transformer decoder showing that their benefits can compound thanks to motion-guided vector quantization. We show that our architecture effectively leverages the synergy between motion and tokenization, improving upon the state of the art on both synthetic and real datasets. Our approach enables the emergence of interpretable object-specific mid-level features, demonstrating the benefits of motion-guidance (no labeling) and quantization (interpretability, memory efficiency)." @default.
- W4386075574 created "2023-08-23" @default.
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- W4386075574 date "2023-06-01" @default.
- W4386075574 modified "2023-09-27" @default.
- W4386075574 title "Object Discovery from Motion-Guided Tokens" @default.
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- W4386075574 doi "https://doi.org/10.1109/cvpr52729.2023.02200" @default.
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