Few-Shot Personalized Saliency Prediction using Person Similarity based on Collaborative Multi-Output Gaussian Process Regression
Few-Shot Personalized Saliency Prediction using Person Similarity based on Collaborative Multi-Output Gaussian Process Regression
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DOI：
10.1109/icip42928.2021.9506583
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Yuya Moroto;Keisuke Maeda;Takahiro Ogawa;M. Haseyama
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文献类型：
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作者：
Yuya Moroto;Keisuke Maeda;Takahiro Ogawa;M. Haseyama
提出了一种基于协作多输出高斯过程回归的人物相似度小样本个性化显著性预测方法。与一般显着图的预测相反，个性化显着图（PSM）的预测是一个具有挑战性的问题，因为训练凝视数据的量由于新人的负担而有限，由于其在个体之间的异质性而成为关注的焦点。因此，所提出的方法侧重于人之间的注视倾向的相似性。在所提出的方法中，协同高斯过程回归（CoMOGP）采用PSM预测。CoMOGP使得能够将目标人与其他人之间的注视趋势的相似性表示为权重，然后通过使用从图像获得的视觉特征作为输入来考虑每个图像的相似性。基于CoMOGP的少拍PSM预测的贡献是双重的。1)CoMOGP是一种概率方法，可以避免对少量训练数据的过拟合。2)每个图像的相似性可以通过使用视觉特征作为输入来考虑。在使用开放数据集的实验中，所提出的方法优于比较方法，包括最先进的方法。
A few-shot personalized saliency prediction method using person similarity based on collaborative multi-output Gaussian process regression is presented in this paper. Contrary to prediction of general saliency maps, that of personalized saliency maps (PSMs), which is a focus of attention owing to its heterogeneity among individuals, is a challenging problem since the amount of training gaze data is limited due to the burden on new persons. Thus, the proposed method focuses on the similarity of gaze tendency between persons. In the proposed method, collaborative Gaussian process regression (CoMOGP) is adopted for PSM prediction. CoMOGP enables to represent similarity of gaze tendency between the target person and other persons as weights, and then consider the similarity for each image by using visual features obtained from images as inputs. The contributions of the few-shot PSM prediction based on CoMOGP are two-folds. 1) CoMOGP, which is one of probabilistic methods, can avoid the overfitting to small amount of training data. 2) Similarity for each image can be considered by using visual features as inputs. In the experiment using the open dataset, the proposed method outperforms comparative methods including the state-of-the-art method.