Nov 2, 2022 · Abstract: Cross-domain object detection aims to transfer knowledge from a labeled dataset to an unlabeled dataset.
(1) We propose a novel Dual Instance-Consistent Net- work to address the domain bias between the source and target domains for cross-domain object detection. (2) ...
In this article, we propose a novel Dual Instance-Consistent Network for cross-domain object detection, which consists of three main components.
We propose a teacher–student framework named dual adaptive branch (DAB), which uses domain adversarial learning to address the domain distribution.
Cross domain object detection is a realistic and challenging task in the wild. It suffers from performance degradation.
Jul 13, 2024 · Domain adaptive object detection (DAOD) aims to infer a robust detector on the target domain with the labelled source datasets.
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In this paper, we propose a Consistent and Contrastive Teacher with Fourier Transform (CCTF) method to address these challenges for high-performance cross- ...
Missing: Dual | Show results with:Dual
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What is cross domain object detection?
Jul 10, 2024 · We present a novel general framework termed Multi-Granularity Confidence Alignment Mean Teacher (MGCAMT) for cross domain object detection.
Different levels of domain classifiers are further assigned adaptive weights to coordinate the transferability and discriminability of the adaptive detectors.
Missing: Dual | Show results with:Dual