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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15404 |
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| _version_ | 1866911711716442112 |
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| author | Ashraf, Tajamul Manna, Rajes Purkayastha, Partha Sarathi Tariq, Tavaheed Bashir, Janibul |
| author_facet | Ashraf, Tajamul Manna, Rajes Purkayastha, Partha Sarathi Tariq, Tavaheed Bashir, Janibul |
| contents | Source-free object detection (SFOD) faces persistent challenges due to class imbalance-driven context bias and instability in teacher-student training under noisy pseudo-labels. Existing techniques tend to ignore context bias and class-imbalance shifts, especially in medical data. To tackle this, we propose Grounded Teacher (GT), a bias-aware source-free framework that grounds the teacher model through relational and semantic regularization. To explicitly model directional confusion between classes, GT introduces a Relational Context Module (RCM) that maintains an exponential moving average (EMA) estimate of cross-domain contextual bias. Building upon this, a Semantic Augmentation (SA) strategy selectively augments minority and confusable classes through adaptive MixUp in both source-similar and source-dissimilar target regions, improving minority recall without overfitting dominant categories. To stabilize learning under biased pseudo-labels, we design a Semantic-Aware Loss (SAL) that applies diagonally normalized weights, preventing gradient explosion while emphasizing minority-majority corrections. Additionally, a frozen Expert branch derived from large vision foundation models (LVFMs) serves as a supervisory reference during training, refining pseudo-label quality without adding inference overhead. GT's behavior-driven bias quantification makes it broadly applicable across domains without relying on dataset priors. Evaluations on Cityscapes-to-Foggy (50.8 mAP) and medical transfers (+5.9 AP50 on DDSM-to-INBreast) show consistent gains and improved minority-class detection, with less than 12\% additional training cost. Code and model are available at https://github.com/Tajamul21/Grounded-Teacher. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15404 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Context Aware Grounded Teacher for Source Free Object Detection Ashraf, Tajamul Manna, Rajes Purkayastha, Partha Sarathi Tariq, Tavaheed Bashir, Janibul Computer Vision and Pattern Recognition Source-free object detection (SFOD) faces persistent challenges due to class imbalance-driven context bias and instability in teacher-student training under noisy pseudo-labels. Existing techniques tend to ignore context bias and class-imbalance shifts, especially in medical data. To tackle this, we propose Grounded Teacher (GT), a bias-aware source-free framework that grounds the teacher model through relational and semantic regularization. To explicitly model directional confusion between classes, GT introduces a Relational Context Module (RCM) that maintains an exponential moving average (EMA) estimate of cross-domain contextual bias. Building upon this, a Semantic Augmentation (SA) strategy selectively augments minority and confusable classes through adaptive MixUp in both source-similar and source-dissimilar target regions, improving minority recall without overfitting dominant categories. To stabilize learning under biased pseudo-labels, we design a Semantic-Aware Loss (SAL) that applies diagonally normalized weights, preventing gradient explosion while emphasizing minority-majority corrections. Additionally, a frozen Expert branch derived from large vision foundation models (LVFMs) serves as a supervisory reference during training, refining pseudo-label quality without adding inference overhead. GT's behavior-driven bias quantification makes it broadly applicable across domains without relying on dataset priors. Evaluations on Cityscapes-to-Foggy (50.8 mAP) and medical transfers (+5.9 AP50 on DDSM-to-INBreast) show consistent gains and improved minority-class detection, with less than 12\% additional training cost. Code and model are available at https://github.com/Tajamul21/Grounded-Teacher. |
| title | Context Aware Grounded Teacher for Source Free Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.15404 |