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Exploring class-agnostic pixels for scribble-supervised high-resolution salient object detection

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Abstract

Successful salient object detection is largely dependent on large-scale fine-grained annotated datasets. However, pixel-level annotation is a laborious process compared with weak labels and scant research has been done on high-resolution images. To mitigate these drawbacks, we propose a distinctive network to explore salient object in high-resolution images under scribble-supervised and relabel a previous high-resolution dataset with scribbles, namely Scr-HRSOD, in which each image is labelled in a few seconds. Since scribble labels lack structural information about objects, a boundary structure maintenance branch with shallow layers is introduced to capture low-level spatial details. Within the constraint of boundary branches, a lightweight contextual semantic branch process compressed inputs to obtain high-level semantic context and iteratively propagates the partially annotated pixels to surrounding similar regions, which are then employed as pseudo-labels to supervise the network. Extensive evaluations on five datasets illustrate the effectiveness of our introduced method. On HRSOD datasets, we achieve higher 0.861 Fmax and 0.887 Sm values, which outperforms the existing foremost weakly supervised methods and even the fully supervised methods.

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Notes

  1. The annotation tool we employed is the scribble annotation tool in Image Labeler in Matlab R2019b.

  2. The Scr-HRSOD datasets are publicly available: https://github.com/YQP-CV/Scribble-Supervised-HRSOD and our code is about to be open source.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (No.62002100), the National Natural Science Foundation of China (No.61802111) and the Science and Technology Foundation of Henan Province of China (No.212102210156). National Natural Science Foundation of China (No.62176088).

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  1. School of Artificial Intelligence, Henan University, Kaifeng, 475004, China

    Qingpeng Yang, Yi Zhou, Xiuli Chai, Miaohui Zhang, Wanjun Zhang & Jun Wang

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  1. Qingpeng Yang
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  2. Yi Zhou
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Correspondence to Jun Wang.

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Yang, Q., Zhou, Y., Chai, X. et al. Exploring class-agnostic pixels for scribble-supervised high-resolution salient object detection. Neural Comput & Applic 35, 3469–3482 (2023). https://doi.org/10.1007/s00521-022-07915-w

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