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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 3: AAAI-22 Technical Tracks 3

Energy-Based Generative Cooperative Saliency Prediction

February 1, 2023

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Authors

Jing Zhang

Australian National University


Jianwen Xie

Baidu Research


Zilong Zheng

UCLA


Nick Barnes

Australian National University


DOI:

10.1609/aaai.v36i3.20237


Abstract:

Conventional saliency prediction models typically learn a deterministic mapping from an image to its saliency map, and thus fail to explain the subjective nature of human attention. In this paper, to model the uncertainty of visual saliency, we study the saliency prediction problem from the perspective of generative models by learning a conditional probability distribution over the saliency map given an input image, and treating the saliency prediction as a sampling process from the learned distribution. Specifically, we propose a generative cooperative saliency prediction framework, where a conditional latent variable model~(LVM) and a conditional energy-based model~(EBM) are jointly trained to predict salient objects in a cooperative manner. The LVM serves as a fast but coarse predictor to efficiently produce an initial saliency map, which is then refined by the iterative Langevin revision of the EBM that serves as a slow but fine predictor. Such a coarse-to-fine cooperative saliency prediction strategy offers the best of both worlds. Moreover, we propose a ``cooperative learning while recovering" strategy and apply it to weakly supervised saliency prediction, where saliency annotations of training images are partially observed. Lastly, we find that the learned energy function in the EBM can serve as a refinement module that can refine the results of other pre-trained saliency prediction models. Experimental results show that our model can produce a set of diverse and plausible saliency maps of an image, and obtain state-of-the-art performance in both fully supervised and weakly supervised saliency prediction tasks.

Topics: AAAI

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HOW TO CITE:

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes Energy-Based Generative Cooperative Saliency Prediction Proceedings of the AAAI Conference on Artificial Intelligence (2022) 3280-3290.

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes Energy-Based Generative Cooperative Saliency Prediction AAAI 2022, 3280-3290.

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes (2022). Energy-Based Generative Cooperative Saliency Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 3280-3290.

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes. Energy-Based Generative Cooperative Saliency Prediction. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.3280-3290.

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes. 2022. Energy-Based Generative Cooperative Saliency Prediction. "Proceedings of the AAAI Conference on Artificial Intelligence". 3280-3290.

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes. (2022) "Energy-Based Generative Cooperative Saliency Prediction", Proceedings of the AAAI Conference on Artificial Intelligence, p.3280-3290

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes, "Energy-Based Generative Cooperative Saliency Prediction", AAAI, p.3280-3290, 2022.

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes. "Energy-Based Generative Cooperative Saliency Prediction". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.3280-3290.

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes. "Energy-Based Generative Cooperative Saliency Prediction". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 3280-3290.

Jing Zhang||Jianwen Xie||Zilong Zheng||Nick Barnes. Energy-Based Generative Cooperative Saliency Prediction. AAAI[Internet]. 2022[cited 2023]; 3280-3290.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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