Abstract
The texture scarcity properties make transparent object localization a challenging task in the computer vision community. This paper addresses this task in two aspects. (i) Additional guidance cues: we propose a Simulation Heatmap Guidance (SHG) to improve the localization ability of the model. Concretely, the target’s extreme points and inference centroids are used to generate simulation heatmaps to offer additional position guides. A high recall is rewarded even in extreme cases. (ii) Enhanced attention: we propose a Context Spatial Attention (CSA) combined with a unique backbone to build dependencies between feature points and to boost multi-scale attention fusion. CSA is a lightweight module and brings apparent perceptual gain. Experiments show that our method achieves more accurate detection for cluttered transparent objects in various scenarios and background settings, outperforming the existing methods.
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Acknowledgement
The work was partly supported by the National Natural Science Foundation of China (No. 62175037) and Shanghai Science and Technology Innovation Action Plan (No. 20JC1416500).
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Chen, S., Li, D., Ju, B., Jiang, L., Zhao, D. (2023). Transparent Object Detection with Simulation Heatmap Guidance and Context Spatial Attention. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_1
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