TETRIS: Towards Exploring the Robustness of Interactive Segmentation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909161786179584 |
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| author | Moskalenko, Andrey Shakhuro, Vlad Vorontsova, Anna Konushin, Anton Antonov, Anton Krapukhin, Alexander Shepelev, Denis Soshin, Konstantin |
| author_facet | Moskalenko, Andrey Shakhuro, Vlad Vorontsova, Anna Konushin, Anton Antonov, Anton Krapukhin, Alexander Shepelev, Denis Soshin, Konstantin |
| contents | Interactive segmentation methods rely on user inputs to iteratively update the selection mask. A click specifying the object of interest is arguably the most simple and intuitive interaction type, and thereby the most common choice for interactive segmentation. However, user clicking patterns in the interactive segmentation context remain unexplored. Accordingly, interactive segmentation evaluation strategies rely more on intuition and common sense rather than empirical studies (e.g., assuming that users tend to click in the center of the area with the largest error). In this work, we conduct a real user study to investigate real user clicking patterns. This study reveals that the intuitive assumption made in the common evaluation strategy may not hold. As a result, interactive segmentation models may show high scores in the standard benchmarks, but it does not imply that they would perform well in a real world scenario. To assess the applicability of interactive segmentation methods, we propose a novel evaluation strategy providing a more comprehensive analysis of a model's performance. To this end, we propose a methodology for finding extreme user inputs by a direct optimization in a white-box adversarial attack on the interactive segmentation model. Based on the performance with such adversarial user inputs, we assess the robustness of interactive segmentation models w.r.t click positions. Besides, we introduce a novel benchmark for measuring the robustness of interactive segmentation, and report the results of an extensive evaluation of dozens of models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_06132 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TETRIS: Towards Exploring the Robustness of Interactive Segmentation Moskalenko, Andrey Shakhuro, Vlad Vorontsova, Anna Konushin, Anton Antonov, Anton Krapukhin, Alexander Shepelev, Denis Soshin, Konstantin Computer Vision and Pattern Recognition Human-Computer Interaction 68T45 I.4.6 Interactive segmentation methods rely on user inputs to iteratively update the selection mask. A click specifying the object of interest is arguably the most simple and intuitive interaction type, and thereby the most common choice for interactive segmentation. However, user clicking patterns in the interactive segmentation context remain unexplored. Accordingly, interactive segmentation evaluation strategies rely more on intuition and common sense rather than empirical studies (e.g., assuming that users tend to click in the center of the area with the largest error). In this work, we conduct a real user study to investigate real user clicking patterns. This study reveals that the intuitive assumption made in the common evaluation strategy may not hold. As a result, interactive segmentation models may show high scores in the standard benchmarks, but it does not imply that they would perform well in a real world scenario. To assess the applicability of interactive segmentation methods, we propose a novel evaluation strategy providing a more comprehensive analysis of a model's performance. To this end, we propose a methodology for finding extreme user inputs by a direct optimization in a white-box adversarial attack on the interactive segmentation model. Based on the performance with such adversarial user inputs, we assess the robustness of interactive segmentation models w.r.t click positions. Besides, we introduce a novel benchmark for measuring the robustness of interactive segmentation, and report the results of an extensive evaluation of dozens of models. |
| title | TETRIS: Towards Exploring the Robustness of Interactive Segmentation |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction 68T45 I.4.6 |
| url | https://arxiv.org/abs/2402.06132 |