Remedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection

• URL: http://arxiv.org/abs/2603.18541v1
• Date: Thu, 19 Mar 2026 06:55:32 GMT
• Title: Remedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection
• Abstract summary: Cross-domain few-shot object detection (CD-FSOD) aims to adapt pretrained detectors from a source domain to target domains with limited annotations.<n>We find a previously overlooked phenomenon: models exhibit imprecise and unfocused attention in target domains.<n>Our bio-inspired approach transforms astigmatic attention into focused patterns, substantially improving adaptation to target domains.
• Score: 19.113214017897118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain few-shot object detection (CD-FSOD) aims to adapt pretrained detectors from a source domain to target domains with limited annotations, suffering from severe domain shifts and data scarcity problems. In this work, we find a previously overlooked phenomenon: models exhibit dispersed and unfocused attention in target domains, leading to imprecise localization and redundant predictions, just like a human cannot focus on visual objects. Therefore, we call it the target-domain Astigmatism problem. Analysis on attention distances across transformer layers reveals that regular fine-tuning inherently shows a trend to remedy this problem, but results are still far from satisfactory, which we aim to enhance in this paper. Biologically inspired by the human fovea-style visual system, we enhance the fine-tuning's inherent trend through a center-periphery attention refinement framework, which contains (1) a Positive Pattern Refinement module to reshape attention toward semantic objects using class-specific prototypes, simulating the visual center region; (2) a Negative Context Modulation module to enhance boundary discrimination by modeling background context, simulating the visual periphery region; and (3) a Textual Semantic Alignment module to strengthen center-periphery distinction through cross-modal cues. Our bio-inspired approach transforms astigmatic attention into focused patterns, substantially improving adaptation to target domains. Experiments on six challenging CD-FSOD benchmarks consistently demonstrate improved detection accuracy and establish new state-of-the-art results.

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