Researchers propose gavagai to improve Vision-Language model alignment
Researchers introduced GAVAGAI, a method to improve how vision-language models link words to visual referents. The paper argues current models struggle because training objectives lack a direct way to map words to objects. GAVAGAI treats each scene as an assignment problem, using entropic optimal transport with two constraints: words can be assigned to a ‘NOTHING’ category if they don’t refer to…
1 source primary source
Key points
- GAVAGAI uses optimal transport to map words to visual regions with two constraints: ‘NOTHING’ for non-visible words and bounded region capacity
- Method improves word-to-picture accuracy on SAYCam corpus from 32.4% to 48.7% and localization IoU from 0.18 to 0.38
- Paper argues current vision-language models lack direct pressure to ground words, citing a collapse to 0.000 accuracy in naive implementations
Read the original at arXiv cs.CL · by Rajdeep Pal, Jotiraditya Banerjee, Saptarshi Pani primary sourceOpen source ↗
Topics · follow one to build your own front page
GAVAGAI
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Research
All →- Researchers introduce ResMem to turn retrieved knowledge into parametric memory · 1 src
- Researchers release BrainGFM, a generative model for brain imaging · 1 src
- Taurus harness cuts CT annotation costs by half with LLM review · 1 src
- Researchers introduce RE-SciVQA for better scientific visual question answering · 1 src
- Researchers propose nuresa layer for memory transfer in AI models · 1 src
Comments
via GitHub Discussions