Utilizing Class-Agnostic Point-to-Box Regressors as Object Proposal Generators

Abstract

Object detectors suffer from reduced performance when they are utilized in real-world scenarios, where out-of-distribution (OOD) data exist together with in-distribution (ID) data. In a closed-world setting, the information present in data is not fully exploited, which results in unreliable models. The demand for models operating on both ID and OOD data is inevitable. We propose Class-Agnostic Point-to-Box Regressor (CA-PBR) for guiding the models to leverage unattended informative data in images without being restricted by class information. CA-PBR trains a point-to-box regressor in a class-agnostic fashion to generate credible object proposals. We additionally show that querying CA-PBR with a set of points obtained from a grid that is created on an image results in detection of novel instances with high diversity. Our experimental results show that utilizing CA-PBR as object proposal generator improves the detection of both known (ID) and novel (OOD) instances when trained on MS-COCO using only 10% box-level labeled images.