DeepGaze3.5-VL treats visual scanpaths as discrete token sequences predicted autoregressively by vision-language models, achieving 2.18 bits IG on MIT1003 and outperforming prior specialized models even with matched encoders.
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DeepGaze3.5-VL: Modeling Scanpaths via Autoregressive Token Prediction
DeepGaze3.5-VL treats visual scanpaths as discrete token sequences predicted autoregressively by vision-language models, achieving 2.18 bits IG on MIT1003 and outperforming prior specialized models even with matched encoders.