Sketch-qnet: A Quadruplet Convnet For Color Sketch-based Image Retrieval
Anibal Fuentes, Jose M. Saavedra . 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2021 – 1 citation
[Paper]
Architectures based on siamese networks with triplet loss have shown outstanding performance on the image-based similarity search problem. This approach attempts to discriminate between positive (relevant) and negative (irrelevant) items. However, it undergoes a critical weakness. Given a query, it cannot discriminate weakly relevant items, for instance, items of the same type but different color or texture as the given query, which could be a serious limitation for many real-world search applications. Therefore, in this work, we present a quadruplet-based architecture that overcomes the aforementioned weakness. Moreover, we present an instance of this quadruplet network, which we call Sketch-QNet, to deal with the color sketch-based image retrieval (CSBIR) problem, achieving new state-of-the-art results.