Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer

• URL: http://arxiv.org/abs/2305.07598v4
• Date: Wed, 29 Nov 2023 08:56:29 GMT
• Title: Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer
• Authors: Hakjin Lee, Minki Song, Jamyoung Koo, Junghoon Seo
• Abstract summary: The application of DETR in detecting rotated objects has demonstrated suboptimal performance relative to established oriented object detectors. We introduce a Hausdorff distance-based cost for Hungarian matching, which more accurately quantifies the discrepancy between predictions and ground truths. We propose an adaptive query denoising technique, employing Hungarian matching to selectively filter out superfluous noised queries that no longer contribute to model improvement.
• Score: 4.519754139322585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Detection Transformer (DETR) has emerged as a pivotal role in object detection tasks, setting new performance benchmarks due to its end-to-end design and scalability. Despite its advancements, the application of DETR in detecting rotated objects has demonstrated suboptimal performance relative to established oriented object detectors. Our analysis identifies a key limitation: the L1 cost used in Hungarian Matching leads to duplicate predictions due to the square-like problem in oriented object detection, thereby obstructing the training process of the detector. We introduce a Hausdorff distance-based cost for Hungarian matching, which more accurately quantifies the discrepancy between predictions and ground truths. Moreover, we note that a static denoising approach hampers the training of rotated DETR, particularly when the detector's predictions surpass the quality of noised ground truths. We propose an adaptive query denoising technique, employing Hungarian matching to selectively filter out superfluous noised queries that no longer contribute to model improvement. Our proposed modifications to DETR have resulted in superior performance, surpassing previous rotated DETR models and other alternatives. This is evidenced by our model's state-of-the-art achievements in benchmarks such as DOTA-v1.0/v1.5/v2.0, and DIOR-R.

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