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Multi-Scale Cross Distillation for Object Detection in Aerial Images

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15107))

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Abstract

Object detection in aerial images is a longstanding yet challenging task. Despite the significant advancements in recent years, most works still show unsatisfactory performance due to the scale variation of objects. A standard strategy to address this problem is multi-scale training, aiming to learn scale-invariant feature representations. Albeit achieving inspiring improvements, such a multi-scale strategy is impractical for real application as inference time increases considerably. Besides, the original images are resized to different scales and subsequently trained separately, lacking information interaction across different scales. This paper presents a novel method called multi-scale cross distillation (MSCD) to address the issues mentioned above. MSCD combines the merits of multi-scale training and knowledge distillation, enabling single-scale inference to achieve comparable or superior performance than multi-scale inference. Specifically, we first construct a parallel multi-branch architecture, in which each branch shares the same parameters yet takes images with different scales as input. Furthermore, we design an adaptive cross-scale distillation module that adaptively integrates the knowledge of different branches into one. Thus, the detectors trained with MSCD only require single-scale inference. Extensive experiments demonstrate the effectiveness of MSCD. Without bells and whistles, MSCD can facilitate prevalent two-stage detectors to outperform corresponding single-scale models by \(\sim \)5 and \(\sim \)7 mAP improvement on DOTA and DIOR-R datasets, respectively.

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Notes

  1. 1.

    It is worth noting that the RPN only predicts foreground and background, which does not make predictions for all categories. Thus, for each prediction of RPN, \(y_i^{m, R}=[c_i^{m, R}, b_i^{m, R}]\in \mathbb {R}^{2+5}\).

  2. 2.

    In this paper, we use the gray font to indicate that variables do not participate in gradient back-propagation.

  3. 3.

    When conducting multi-scale training and testing, the original images are first resized to three scales, i.e., (0.5, 1.0, 1.5), which are then cropped to 1,024 \(\times \) 1,024 patches with a stride of 524.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 12302252.

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Authors and Affiliations

  1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, 410000, China

    Kun Wang, Zi Wang, Zhang Li, Xichao Teng & Yang Li

  2. Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha, 410000, China

    Kun Wang, Zi Wang, Zhang Li, Xichao Teng & Yang Li

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  1. Kun Wang
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  2. Zi Wang
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  3. Zhang Li
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  4. Xichao Teng
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  5. Yang Li
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Correspondence to Zhang Li .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Wang, K., Wang, Z., Li, Z., Teng, X., Li, Y. (2025). Multi-Scale Cross Distillation for Object Detection in Aerial Images. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15107. Springer, Cham. https://doi.org/10.1007/978-3-031-72967-6_25

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