Diffusion-CAM is the first method for visual explanations in dMLLMs, using differentiable probing of intermediates plus four refinement modules to produce activation maps that outperform prior CAM approaches in localization and fidelity.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.
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Diffusion-CAM: Faithful Visual Explanations for dMLLMs
Diffusion-CAM is the first method for visual explanations in dMLLMs, using differentiable probing of intermediates plus four refinement modules to produce activation maps that outperform prior CAM approaches in localization and fidelity.
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Dual-Stream EEG Decoding for 3D Visual Perception
Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.