Deep Ranking for Style-Aware Room Recommendations (Student Abstract)

Ilkay Yildiz, Esra Ataer Cansizoglu, Hantian Liu, Peter B. Golbus, Ozan Tezcan, Jae-Woo Choi

Abstract: We present a deep learning based room image retrieval framework that is based on style understanding. Given a dataset of room images labeled by interior design experts, we map the noisy style labels to comparison labels. Our framework learns the style spectrum of each image from the generated comparisons and makes significantly more accurate recommendations compared to discrete classification baselines.