Open-Set Object Detection By Aligning Known Class Representations

TL;DR

This paper introduces a novel semantic clustering approach with class decorrelation and object focus modules for open-set object detection, improving unknown object detection and classification on MS-COCO and PASCAL VOC datasets.

Contribution

It proposes a new semantic clustering-based method with class decorrelation and object focus modules, along with a novel evaluation technique and metric for open-set object detection.

Findings

Significant performance improvements on MS-COCO dataset

Enhanced detection of unknown objects with the proposed modules

Effective mitigation of misclassification risks

Abstract

Open-Set Object Detection (OSOD) has emerged as a contemporary research direction to address the detection of unknown objects. Recently, few works have achieved remarkable performance in the OSOD task by employing contrastive clustering to separate unknown classes. In contrast, we propose a new semantic clustering-based approach to facilitate a meaningful alignment of clusters in semantic space and introduce a class decorrelation module to enhance inter-cluster separation. Our approach further incorporates an object focus module to predict objectness scores, which enhances the detection of unknown objects. Further, we employ i) an evaluation technique that penalizes low-confidence outputs to mitigate the risk of misclassification of the unknown objects and ii) a new metric called HMP that combines known and unknown precision using harmonic mean. Our extensive experiments demonstrate that the proposed model achieves significant improvement on the MS-COCO & PASCAL VOC dataset for the OSOD task.