Jag: Direct Box Prediction for Efficient Visual Grounding
Visual grounding combines image--language understanding with geometric prediction, yet autoregressive multimodal models often express a box as a coordinate sequence.
ProofPaper ↗
Key points
- Sequential decoding adds computation to the multimodal processing needed for localization.
- Adapting these representations to box geometry offers a route to direct prediction.
- We present Jag, which adapts a compact pretrained multimodal model for direct continuous box prediction.
- These results show how supervision tailored to the geometric answer can specialize a compact pretrained multimodal model for accurate grounding and efficient direct prediction.
Sources (2)
- [1]Jag: Direct Box Prediction for Efficient Visual GroundingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 08:54 AM
Visual grounding combines image--language understanding with geometric prediction, yet autoregressive multimodal models often express a box as a coordinate sequence.
Sequential decoding adds computation to the multimodal processing needed for localization.
- [2]Jag: Direct Box Prediction for Efficient Visual GroundingHugging Face Daily Papers · Oct 9, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
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