Self-improving object detection via disagreement reconciliation

• URL: http://arxiv.org/abs/2302.10624v1
• Date: Tue, 21 Feb 2023 12:20:46 GMT
• Title: Self-improving object detection via disagreement reconciliation
• Authors: Gianluca Scarpellini, Stefano Rosa, Pietro Morerio, Lorenzo Natale, Alessio Del Bue
• Abstract summary: This paper studies how to automatically fine-tune a pre-existing object detector while exploring and acquiring images in a new environment. By assuming that pseudo-labels for the same object must be consistent across different views, we devise a novel mechanism for producing refined predictions from the consensus among observations. Our approach improves the off-the-shelf object detector by 2.66% in terms of mAP and outperforms the current state of the art without relying on ground-truth annotations.
• Score: 30.971936386281275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data. This paper studies how to automatically fine-tune a pre-existing object detector while exploring and acquiring images in a new environment without relying on human intervention, i.e., in a self-supervised fashion. In our setting, an agent initially explores the environment using a pre-trained off-the-shelf detector to locate objects and associate pseudo-labels. By assuming that pseudo-labels for the same object must be consistent across different views, we devise a novel mechanism for producing refined predictions from the consensus among observations. Our approach improves the off-the-shelf object detector by 2.66% in terms of mAP and outperforms the current state of the art without relying on ground-truth annotations.

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