The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
What Do Different Evaluation Metrics Tell Us About Saliency Models?
Open accessAbstract
How best to evaluate a saliency model's ability to predict where humans look in images is an open research question. The choice of evaluation metric depends on how saliency is defined and how the ground truth is represented. Metrics differ in how they rank saliency models, and this results from how false positives and false negatives are treated, whether viewing biases are accounted for, whether spatial deviations are factored in, and how the saliency maps are pre-processed. In this paper, we provide an analysis of 8 different evaluation metrics and their properties. With the help of systematic experiments and visualizations of metric computations, we add interpretability to saliency scores and more transparency to the evaluation of saliency models. Building off the differences in metric properties and behaviors, we make recommendations for metric selections under specific assumptions and for specific applications.
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Palazzi, A., Abati, D., Calderara, S., Solera, F., & Cucchiara, R. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2845370 · Cited by 313
Evaluating saliency map explanations for convolutional neural networks: a user study
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Self-Supervised Representation Learning for Ultrasound Video
Jiao, J., Droste, R., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2020) · 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098666 · Cited by 56
Human Gaze Assisted Artificial Intelligence: A Review
Zhang, R., Saran, A., Liu, B., Zhu, Y., Guo, S., Niekum, S., Ballard, D., & Hayhoe, M. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/689 · Cited by 48
Metadata
- Authors
- Bylinskii, Z., Judd, T., Oliva, A., Torralba, A., & Durand, F.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2019-03-01
- Volume
- 41
- Issue
- 3
- Pages
- 740-757
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2336525740
- Cited by
- 802
- References
- 90
- FWCI
- 44.36
- Type
- journal article
- Language
- en
- Open access
- green
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