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This work suggests a psychologically-inspired framework for approximating thought that is embodied, multi-modal, and at its core— generative, and proposes that this iterative cycle of generative questioning will result in this model achieving a milestone of human thought.
G Gazette, the first gaze-to-text decoding framework, is introduced, based on multimodal large language models (MLLMs), which learns to decode gaze scanpaths into natural language for goals that may extend beyond categorical labels and require articulation in natural language.
This paper finetune two report generation models with an attention-alignment loss that regularizes the model attention over image patches to match the distribution of pathologist attention, providing stronger visual support for the generated reports by highlighting the regions that most influence the output.
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