Attention Alignment Between Humans and Vision-Language Models
Visual perception depends on top-down goals and bottom-up sensory mechanisms. Vision-language models implement both, allowing us to treat each component as a separable hypothesis about what drives where we look. We compared spatial attention maps from six vision-language models against human fixation heatmaps recorded on 200 images during two tasks (general description and social captioning). The six models spanned a 2$\times$2 factorial of CNN vs.\ ViT encoders crossed with LSTM vs.\ Transformer decoders, plus Molmo 7B-D and Qwen3.5 9B. We found that both decoder and encoder architecture shaped alignment, but decoder choice dominated. LSTM vs.\ Transformer decoders increased alignment by...
| Authors | Isaac R. Christian, Udith Haputhanthrige, Hanna Hornfeld et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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