Inverted-Attention Transformers can Learn Object Representations

A widely adopted method within OCL is slot attention, which utilizes attention mechanisms to iteratively refine slot representations However, a major

Inverted-Attention Transformers can Learn Object Representations PARTS: Unsupervised segmentation with slots, attention and independence maximization Abstract: From an early age, humans perceive the visual world as composed

A widely adopted method within OCL is slot attention, which utilizes attention mechanisms to iteratively refine slot representations However, a major

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Researchers from Rutgers University propose a slot-based autoencoder architecture called SLot Attention TransformEr The SLATE model

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to model slot type attention and slot value attention simultaneously and capture their Hybrid-Attention based on Slot-Attention and Link-Attention