Indiscernible Object Counting in Underwater Scenes

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Indiscernible Object Counting in Underwater Scenes
Title:
Indiscernible Object Counting in Underwater Scenes
Journal Title:
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Keywords:
Publication Date:
22 August 2023
Citation:
Sun, G., An, Z., Liu, Y., Liu, C., Sakaridis, C., Fan, D.-P., & Gool, L. V. (2023). Indiscernible Object Counting in Underwater Scenes. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52729.2023.01325
Abstract:
Recently, indiscernible scene understanding has attracted a lot of attention in the vision community. We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundings. Due to a lack of appropriate IOC datasets, we present a large-scale dataset IOCfish5K which contains a total of 5,637 high-resolution images and 659,024 annotated center points. Our dataset consists of a large number of indiscernible objects (mainly fish) in underwater scenes, making the annotation process all the more challenging. IOCfish5K is superior to existing datasets with indiscernible scenes because of its larger scale, higher image resolutions, more annotations, and denser scenes. All these aspects make it the most challenging dataset for IOC so far, supporting progress in this area. For benchmarking purposes, we select 14 mainstream methods for object counting and carefully evaluate them on IOCfish5K. Furthermore, we propose IOCFormer, a new strong baseline that combines density and regression branches in a unified framework and can effectively tackle object counting under concealed scenes. Experiments show that IOCFormer achieves state-of-the-art scores on IOCfish5K. The resources are available at github.com/GuoleiSun/Indiscernible-Object-Counting.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2575-7075
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