Li
Live Demo of Composite Image Retrieval (CIR)
Hacker NewsLive Demo of Composite Image Retrieval (CIR)
The recent progress in multi-modality retrieval is to augment image search with text instruction, so called Composite Image Retrieval (CIR). Related research includes https://huggingface.co/BAAI/bge-visualized and Google's https://open-vision-language.github.io/MagicLens/ . We built a live demo for people to play with it and get a feeling of the quality of this retrieval approach.
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, visual, open · Missing: mac, agents, macos54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google, visualize · Missing: mobile apps, ios, personal53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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