MDM distills vision-language datasets via joint embedding clustering, weight-space model interpolation, and geometry-aware distribution matching on the unit hypersphere.
Blip: Bootstrapping language-image pre-training for unified vision- language understanding and generation
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ThinkDeeper introduces a world-model-based reasoning step that predicts future spatial states to improve multimodal visual grounding for autonomous vehicles, achieving top results on Talk2Car and other benchmarks.
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Multimodal Distribution Matching for Vision-Language Dataset Distillation
MDM distills vision-language datasets via joint embedding clustering, weight-space model interpolation, and geometry-aware distribution matching on the unit hypersphere.
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Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles
ThinkDeeper introduces a world-model-based reasoning step that predicts future spatial states to improve multimodal visual grounding for autonomous vehicles, achieving top results on Talk2Car and other benchmarks.