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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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publications
Modeling Object Dissimilarity for Deep Saliency Prediction
Published in Transactions on Machine Learning Research (TMLR), 2022
We introduce a detection-guided saliency prediction network that explicitly models the differences between multiple objects, such as their appearance and size dissimilarities.
Recommended citation: Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang, Seungryong Kim, Mathieu Salzmann, Sabine Süsstrunk. (2022). " Modeling Object Dissimilarity for Deep Saliency Prediction." Transactions on Machine Learning Research (TMLR).
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TempSAL - Uncovering Temporal Information for Deep Saliency Prediction
Published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
This paper introduces a novel saliency prediction model that learns to output saliency maps in sequential time intervals by exploiting human temporal attention patterns.
Recommended citation: Bahar Aydemir, Ludo Hoffstetter, Tong Zhang, Mathieu Salzmann, Sabine Süsstrunk. (2023). "TempSAL - Uncovering Temporal Information for Deep Saliency Prediction." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6461-6470.
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Data Augmentation via Latent Diffusion for Saliency Prediction
Published in Proceedings of the European Conference on Computer Vision, 2024
We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes.
Recommended citation: Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang, Mathieu Salzmann, Sabine Süsstrunk. (2024). "Data Augmentation via Latent Diffusion for Saliency Prediction." European Conference on Computer Vision (ECCV).
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