The What-Where Transformer achieves explicit what-where separation in a ViT-style backbone via concurrent token and attention-map streams, yielding emergent object discovery from attention maps and better weakly-supervised localization.
Object-centric slot diffusion
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A deep sprite-based image decomposition method matches SOTA unsupervised class-aware segmentation on CLEVR, scales linearly with objects, explicitly identifies categories, and fully models images interpretably.
Slot-MLLM introduces a slot-attention-based object-centric visual tokenizer with Q-Former encoder, diffusion decoder, and residual vector quantization for improved local visual comprehension and generation in multimodal LLMs.
citing papers explorer
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What-Where Transformer: A Slot-Centric Visual Backbone for Concurrent Representation and Localization
The What-Where Transformer achieves explicit what-where separation in a ViT-style backbone via concurrent token and attention-map streams, yielding emergent object discovery from attention maps and better weakly-supervised localization.
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Deep sprite-based image models: An analysis
A deep sprite-based image decomposition method matches SOTA unsupervised class-aware segmentation on CLEVR, scales linearly with objects, explicitly identifies categories, and fully models images interpretably.
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Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM
Slot-MLLM introduces a slot-attention-based object-centric visual tokenizer with Q-Former encoder, diffusion decoder, and residual vector quantization for improved local visual comprehension and generation in multimodal LLMs.