cvpr19.pdf
cseweb.ucsd.edu · 6,616 words · saved by 1 readers
N/A
Complete the Look: Scene-based Complementary Product Recommendation Wang-Cheng Kang†∗, Eric Kim‡ , Jure Leskovec‡§ , Charles Rosenberg‡ , Julian McAuley† ‡ Pinterest, § Stanford University, † UC San Diego {wckang,jmcauley}@ucsd.edu, {erickim,jure,crosenberg}@pinterest.com Abstract Product-based Complementary Recommendation Modeling fashion compatibility is challenging due to its complexity and subjectivity. Existing work focuses on…
related reading
- Improving Online Customer Shopping Experience with Computer Vision and Machine Learning Methods | Springer Nature Linklink.springer.com
- Cosmoscosmos.so
- Contra Labs - Powered by Contracontralabs.com
- Black Forest Labs - Frontier AI Labbfl.ai
- Feature-wise transformationsdistill.pub
- Telling Left from Right: Identifying Geometry-Aware Semantic Correspondencetelling-left-from-right.github.io
- Luma | AI Agents for Creative Worklumalabs.ai
- Blueprint-Bench: Testing spatial intelligence in AI models | Andon Labsandonlabs.com
- The Fabricant Intelligent Toolsthefabricant.com
- A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondencesd-complements-dino.github.io
- Piece it Together: Part-Based Concepting with IP-Priorseladrich.github.io
- [2605.20731] TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Designarxiv.org