Conditional similarity networks
Research output: Contribution to journal › Conference article › Research › peer-review
What makes images similar? To measure the similarity between images, they are typically embedded in a featurevector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately.
Original language | English |
---|---|
Journal | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
Pages (from-to) | 1781-1789 |
Number of pages | 9 |
DOIs | |
Publication status | Published - 6 Nov 2017 |
Externally published | Yes |
Event | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States Duration: 21 Jul 2017 → 26 Jul 2017 |
Conference
Conference | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
---|---|
Country | United States |
City | Honolulu |
Period | 21/07/2017 → 26/07/2017 |
Bibliographical note
Publisher Copyright:
© 2017 IEEE.
ID: 301826533