COLIP-2: Olfaction-Vision-Language Embeddings
The Contrastive Olfaction-Language-Image Pre-training 2 (COLIP-2) model is a multimodal embeddings space that places olfaction as a first-class citizen among vision and language. Molecular structure, gas-sensor readings, odor-descriptor language, and images are all trained into a single shared representation space, so that a robot can localize a detected aroma to objects in a scene probabilistically. No ImageNet-scale datasets of paired image-scent examples exists which warrants the need for their collection. Our intent with the release of COLIP-2 is to demonstrate the limit of what can be built for robotics with open-sourced olfactory data in order to ground the argument for why new meth...
| Authors | Kordel Kade France |
|---|---|
| Primary category | cs.RO |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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