TIB: Detecting Unknown Objects via Two-Stream Information BottleneckDownload PDF

Published: 01 Feb 2023, Last Modified: 13 Feb 2023Submitted to ICLR 2023Readers: Everyone
Abstract: Detecting diverse objects, including ones never-seen-before during model training, is critical for the safe application of object detectors. To this end, a task of unsupervised out-of-distribution object detection (OOD-OD) is proposed to detect unknown objects without the reliance on an auxiliary dataset. For this task, it is important to reduce the impact of lacking unknown data for supervision and leverage in-distribution (ID) data to improve the model's discrimination ability. In this paper, we propose a method of Two-Stream Information Bottleneck (TIB), which consists of a standard Information Bottleneck and a dedicated Reverse Information Bottleneck (RIB). Specifically, after extracting the features of an ID image, we first define a standard IB network to disentangle instance representations that are beneficial for localizing and recognizing objects. Meanwhile, we present RIB to obtain simulative OOD features to alleviate the impact of lacking unknown data. Different from standard IB aiming to extract task-relevant compact representations, RIB is to obtain task-irrelevant representations by reversing the optimization objective of the standard IB. Next, to further enhance the discrimination ability, a mixture of information bottlenecks is designed to sufficiently capture object-related information. In the experiments, our method is evaluated on OOD-OD and incremental object detection. The significant performance gains over baselines show the superiorities of our method.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Applications (eg, speech processing, computer vision, NLP)
15 Replies

Loading