Bayesian Detector Combination for Object Detection with Crowdsourced Annotations
Date
2025
Authors
Tan, Z.Q.
Isupova, O.
Carneiro, G.
Zhu, X.
Li, Y.
Editors
Leonardis, A.
Ricci, E.
Roth, S.
Russakovsky, O.
Sattler, T.
Varol, G.
Ricci, E.
Roth, S.
Russakovsky, O.
Sattler, T.
Varol, G.
Advisors
Journal Title
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Conference paper
Citation
Proceedings of the 18th European Conference on Computer Vision, Part LXIII (ECCV 2024), as published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2025 / Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (ed./s), vol.15121 LNCS, pp.329-346
Statement of Responsibility
Zhi Qin Tan, Olga Isupova, Gustavo Carneiro, Xiatian Zhu, and Yunpeng Li
Conference Name
18th European Conference on Computer Vision (ECCV) (29 Sep 2024 - 4 Oct 2024 : Milan, Italy)
Abstract
Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise, especially in crowdsourcing scenarios. Most prior object detection methods assume accurate annotations; A few recent works have studied object detection with noisy crowdsourced annotations, with evaluation on distinct synthetic crowdsourced datasets of varying setups under artificial assumptions. To address these algorithmic limitations and evaluation inconsistency, we first propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations, with the unique ability of automatically inferring the annotators’ label qualities. Unlike previous approaches, BDC is model-agnostic, requires no prior knowledge of the annotators’ skill level, and seamlessly integrates with existing object detection models. Due to the scarcity of real-world crowdsourced datasets, we introduce large synthetic datasets by simulating varying crowdsourcing scenarios. This allows consistent evaluation of different models at scale. Extensive experiments on both real and synthetic crowdsourced datasets show that BDC outperforms existing state-of-the-art methods, demonstrating its superiority in leveraging crowdsourced data for object detection. Our code and data are available at: https://github.com/zhiqin1998/bdc.
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The Author(s), under exclusive license to Springer Nature Switzerland AG 2025