A driving VQA model prunes image tokens using the text query and enhances the survivors with cross-frame attention, reporting 168x token reduction and gains over its baseline.
Driving with llms: Fusing object-level vector modality for explainable au- tonomous driving
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LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement
A driving VQA model prunes image tokens using the text query and enhances the survivors with cross-frame attention, reporting 168x token reduction and gains over its baseline.