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2022

Explaining YOLO: Leveraging Grad-CAM to Explain Object Detections

Kirchknopf, Armin, Slijepcevic, Djordje, Wunderlich, Ilkay et al.

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We investigate the problem of explainability for visual object detectors.

Specifically, we demonstrate on the example of the YOLO object detector how to integrate Grad-CAM into the model architecture and analyze the results.
We show how to compute attribution-based explanations for individual detections and find that the normalization of the results has a great impact on their interpretation.

Explaining YOLO: Leveraging Grad-CAM to Explain Object Detections · Around

Built on

M. T. Ribeiro, S. Singh, and C. Guestrin, “” why should i trust you?” explaining the predictions of any classifier,” in
2016
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Adadi and others, “Peeking inside the black-box: A survey on explainable artificial intelligence (XAI),”
2018
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J. Chen, L. Song, M. Wainwright, and M. Jordan, “Learning to explain: An information-theoretic perspective on model interpretation,” in
2018
Earlier work this paper cites.

Similar

J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” 2018
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R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,”
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H. Tsunakawa, Y. Kameya, H. Lee, Y. Shinya, and N. Mitsumoto, “Contrastive relevance propagation for interpreting predictions by a single-shot object detector,” in
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Then

I. Wunderlich, B. Koch, and S. Schönfeld, “An overview of arithmetic adaptations for inference of convolutional neural networks on re-configurable hardware,” in
Original
2019
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L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu, and M. Pietikäinen, “Deep learning for generic object detection: A survey,”
2020
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V. Petsiuk, R. Jain, V. Manjunatha, V. I. Morariu, A. Mehra, V. Ordonez, and K. Saenko, “Black-box explanation of object detectors via saliency maps,” in
2021
Later among the works it cites.

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