Visual Set Program Synthesizer
A user pointing their phone at a supermarket shelf and asking "Which soda has the least sugar?" poses a difficult challenge for current visual Al assistants. Such queries require not only object recognition, but explicit set-based reasoning such as filtering, comparison, and aggregation. Standard endto-end MLLMs often fail at these tasks because they lack an explicit mechanism for compositional logic. We propose treating visual reasoning as Visual Program Synthesis, where the model first generates a symbolic program that is executed by a separate engine grounded in visual scenes. We also introduce Set-VQA, a new benchmark designed specifically for evaluating set-based visual reasoning. Ex...
| Authors | Zehua Cheng, Wei Dai, Wenhu Zhang et al. |
|---|---|
| Primary category | cs.MM |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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