Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to predict saliency maps on images in new domains that lack sufficient data for data-hungry models. To solve this problem, we propose a few-shot transfer learning paradigm for saliency prediction, which enables efficient transfer of knowledge learned from the existing large-scale saliency datasets to a target domain with limited labeled samples. Specifically, few target domain samples are used as the reference to train a model with a source domain dataset such that the training process can converge to a local minimum in favor of the target domain. Then, the learned model is further fine-tuned with the reference. The proposed framework is gradient-based and model-agnostic. We conduct comprehensive experiments and ablation study on various source domain and target domain pairs. The results show that the proposed framework achieves a significant performance improvement. The code is publicly available at https://github.com/luoyan407/n-reference.
|Original language||English (US)|
|Title of host publication||Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings|
|Editors||Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||18|
|State||Published - 2020|
|Event||16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom|
Duration: Aug 23 2020 → Aug 28 2020
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||16th European Conference on Computer Vision, ECCV 2020|
|Period||8/23/20 → 8/28/20|
Bibliographical noteFunding Information:
Acknowledgments. This research was funded in part by the NSF under Grants 1908711, 1849107, in part by the University of Minnesota Department of Computer Science and Engineering Start-up Fund (QZ), and in part supported by the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.
© 2020, Springer Nature Switzerland AG.
- Deep learning
- Saliency prediction
- n-shot transfer learning