Union-over-Intersections: Object Detection beyond Winner-Takes-All

• URL: http://arxiv.org/abs/2311.18512v2
• Date: Thu, 19 Dec 2024 14:46:05 GMT
• Title: Union-over-Intersections: Object Detection beyond Winner-Takes-All
• Authors: Aritra Bhowmik, Pascal Mettes, Martin R. Oswald, Cees G. M. Snoek,
• Abstract summary: This paper revisits the problem of predicting box locations in object detection architectures.<n>We propose a simpler approach: regress only to the area of intersection between the proposal and the ground truth.<n>Instead of adopting a winner-takes-all strategy, we take the union over the regressed intersections of all boxes in a region to generate the final box outputs.
• Score: 54.89876370237598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper revisits the problem of predicting box locations in object detection architectures. Typically, each box proposal or box query aims to directly maximize the intersection-over-union score with the ground truth, followed by a winner-takes-all non-maximum suppression where only the highest scoring box in each region is retained. We observe that both steps are sub-optimal: the first involves regressing proposals to the entire ground truth, which is a difficult task even with large receptive fields, and the second neglects valuable information from boxes other than the top candidate. Instead of regressing proposals to the whole ground truth, we propose a simpler approach: regress only to the area of intersection between the proposal and the ground truth. This avoids the need for proposals to extrapolate beyond their visual scope, improving localization accuracy. Rather than adopting a winner-takes-all strategy, we take the union over the regressed intersections of all boxes in a region to generate the final box outputs. Our plug-and-play method integrates seamlessly into proposal-based, grid-based, and query-based detection architectures with minimal modifications, consistently improving object localization and instance segmentation. We demonstrate its broad applicability and versatility across various detection and segmentation tasks.

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