To understand visual content, computers need to learn what makes images similar. This similarity learning directly implies a representation of the visual content that captures the inherent structure of the data. We present several approaches that can be applied on top of arbitrary deep metric learning methods and various network architectures. Key issues that these works tackle include improving generalization and transfer to novel data, shared feature learning, and adaptive sampling strategies based on reinforcement learning to effectively utilize large amounts of training data.
Following, we provide a (selective) overview of our research on visual similarity learning. For a comprehensive list, please visit our publication page.
Neural Information Processing Systems (NeurIPS), 2021.
Proceedings of International Conference on Machine Learning (ICML), 2021.
IEEE European Conference on Computer Vision (ECCV), 2020.
Sharing Matters for Generalization in Deep Metric Learning Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
International Conference on Machine Learning (ICML), 2020.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1 , 2020.
In: Pattern Recognition, 102 , 2020.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.
Deep Unsupervised Learning of Visual Similarities Journal Article
In: Pattern Recognition, 78 , 2018.
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), MIT Press, Barcelona, 2016.