Related papers: Revisiting Proposal-based Object Detection

Revisiting Proposal-based Object Detection

URL: http://arxiv.org/abs/2311.18512v1
Date: Thu, 30 Nov 2023 12:40:23 GMT
Title: Revisiting Proposal-based Object Detection
Authors: Aritra Bhowmik, Martin R. Oswald, Pascal Mettes, Cees G. M. Snoek
Abstract summary: We revisit the pipeline for detecting objects in images with proposals. We solve a simple problem where we regress to the area of intersection between proposal and ground truth. Our revisited approach comes with minimal changes to the detection pipeline and can be plugged into any existing method.
Score: 59.97295544455179
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper revisits the pipeline for detecting objects in images with proposals. For any object detector, the obtained box proposals or queries need to be classified and regressed towards ground truth boxes. The common solution for the final predictions is to directly maximize the overlap between each proposal and the ground truth box, followed by a winner-takes-all ranking or non-maximum suppression. In this work, we propose a simple yet effective alternative. For proposal regression, we solve a simpler problem where we regress to the area of intersection between proposal and ground truth. In this way, each proposal only specifies which part contains the object, avoiding a blind inpainting problem where proposals need to be regressed beyond their visual scope. In turn, we replace the winner-takes-all strategy and obtain the final prediction by taking the union over the regressed intersections of a proposal group surrounding an object. Our revisited approach comes with minimal changes to the detection pipeline and can be plugged into any existing method. We show that our approach directly improves canonical object detection and instance segmentation architectures, highlighting the utility of intersection-based regression and grouping.

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