Abstract
Humans can intuitively understand the content of images, and often reach a consensus that some images are more difficult to visual search tasks than others. However, this is quite challenging for computers as it is a subjective task which may be influenced by human emotional factors. Instead of focusing on how the models make reactions on datasets, our method has a capability of assigning scores to samples respectively within a dataset that estimating the difficulty of visual search tasks. Our model shows better performance for predicting visual search difficulty scores of samples produced by human annotators in PASCAL VOC2012. Eventually, we demostrate with experiments that our method has an ability of selecting suitable samples to improve the performance of detectors in a semi-supervised task.
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Acknowledgements
This research was partly supported by Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (KF-2018-03-065) and National Science Foundation, China (No. 61702226).
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Ma, D., Zhang, H., Wu, H., Zhang, T., Sun, J. (2019). Estimating Difficulty Score of Visual Search in Images for Semi-supervised Object Detection. In: Ohara, K., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2019. Lecture Notes in Computer Science(), vol 11669. Springer, Cham. https://doi.org/10.1007/978-3-030-30639-7_1
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