Visual Search

TL;DR for operators When a multimodal model cannot reliably recover product details from video, the first engineering question should not automatically be how to make it reason harder. In a controlled seven-category ablation, retrieving visually similar products and their known attributes raises average micro-F1 from 33.4 to 48.3. Adding interleaved chain-of-thought without retrieval moves the score only to 33.5. The strongest standalone lever in this experiment is better evidence. ...

A customer takes a blurry photo of a sneaker under kitchen lighting. The retailer’s catalog contains polished studio images, clean backgrounds, perfect angles, and the calm confidence of professional photography. The retrieval engine is asked to connect these two worlds. This is where many visual retrieval systems quietly lose money. The technical name is domain adaptive retrieval: learning compact hash codes so that images from one domain can be retrieved meaningfully using queries from another. The practical version is less elegant. Product photos do not look like user photos. Scanned forms do not look like native PDFs. Medical images vary across machines. Warehouse images vary across cameras, lighting, and dirt. Reality has terrible styling. ...