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Authors: Angelo Menezes 1 ; Augusto Peterlevitz 2 ; Mateus Chinelatto 2 and André C. P.L. F. de Carvalho 1

Affiliations: 1 Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil ; 2 Computer Vision Department, Eldorado Research Institute, Campinas, Brazil

Keyword(s): Object Detection, Continual Learning, Continual Object Detection, Replay, Parameter Mining.

Abstract: Continual Object Detection is essential for enabling intelligent agents to interact proactively with humans in real-world settings. While parameter-isolation strategies have been extensively explored in the context of continual learning for classification, they have yet to be fully harnessed for incremental object detection scenarios. Drawing inspiration from prior research that focused on mining individual neuron responses and integrating insights from recent developments in neural pruning, we proposed efficient ways to identify which layers are the most important for a network to maintain the performance of a detector across sequential updates. The presented findings highlight the substantial advantages of layer-level parameter isolation in facilitating incremental learning within object detection models, offering promising avenues for future research and application in real-world scenarios.

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Paper citation in several formats:
Menezes, A.; Peterlevitz, A.; Chinelatto, M. and C. P.L. F. de Carvalho, A. (2024). Efficient Parameter Mining and Freezing for Continual Object Detection. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 466-474. DOI: 10.5220/0012362300003660

@conference{visapp24,
author={Angelo Menezes. and Augusto Peterlevitz. and Mateus Chinelatto. and André {C. P.L. F. de Carvalho}.},
title={Efficient Parameter Mining and Freezing for Continual Object Detection},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={466-474},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012362300003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Efficient Parameter Mining and Freezing for Continual Object Detection
SN - 978-989-758-679-8
IS - 2184-4321
AU - Menezes, A.
AU - Peterlevitz, A.
AU - Chinelatto, M.
AU - C. P.L. F. de Carvalho, A.
PY - 2024
SP - 466
EP - 474
DO - 10.5220/0012362300003660
PB - SciTePress

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