A foveated VLM trained for scene comprehension produces human-like fixations, outperforming models trained for search, classification, or with altered peripheral vision.
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Attention layers do not improve BiLSTM performance on argument unit segmentation and contextualized embeddings show little benefit.
citing papers explorer
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Why We Look Where We Look: Emergent Human-like Fixations of a Foveated Visual Language Model Maximizing Scene Understanding
A foveated VLM trained for scene comprehension produces human-like fixations, outperforming models trained for search, classification, or with altered peripheral vision.
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Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation
Attention layers do not improve BiLSTM performance on argument unit segmentation and contextualized embeddings show little benefit.