Foundation Model Priors Enhance Object Focus in Feature Space for Source-Free Object Detection
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914340985110528 |
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| author | VCR, Sairam Lalla, Rishabh Dayal, Aveen Kulkarni, Tejal Lalla, Anuj Balasubramanian, Vineeth N Khan, Muhammad Haris |
| author_facet | VCR, Sairam Lalla, Rishabh Dayal, Aveen Kulkarni, Tejal Lalla, Anuj Balasubramanian, Vineeth N Khan, Muhammad Haris |
| contents | Current state-of-the-art approaches in Source-Free Object Detection (SFOD) typically rely on Mean-Teacher self-labeling. However, domain shift often reduces the detector's ability to maintain strong object-focused representations, causing high-confidence activations over background clutter. This weak object focus results in unreliable pseudo-labels from the detection head. While prior works mainly refine these pseudo-labels, they overlook the underlying need to strengthen the feature space itself. We propose FALCON-SFOD (Foundation-Aligned Learning with Clutter suppression and Noise robustness), a framework designed to enhance object-focused adaptation under domain shift. It consists of two complementary components. SPAR (Spatial Prior-Aware Regularization) leverages the generalization strength of vision foundation models to regularize the detector's feature space. Using class-agnostic binary masks derived from OV-SAM, SPAR promotes structured and foreground-focused activations by guiding the network toward object regions. IRPL (Imbalance-aware Noise Robust Pseudo-Labeling) complements SPAR by promoting balanced and noise-tolerant learning under severe foreground-background imbalance. Guided by a theoretical analysis that connects these designs to tighter localization and classification error bounds, FALCON-SFOD achieves competitive performance across SFOD benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foundation Model Priors Enhance Object Focus in Feature Space for Source-Free Object Detection VCR, Sairam Lalla, Rishabh Dayal, Aveen Kulkarni, Tejal Lalla, Anuj Balasubramanian, Vineeth N Khan, Muhammad Haris Computer Vision and Pattern Recognition Current state-of-the-art approaches in Source-Free Object Detection (SFOD) typically rely on Mean-Teacher self-labeling. However, domain shift often reduces the detector's ability to maintain strong object-focused representations, causing high-confidence activations over background clutter. This weak object focus results in unreliable pseudo-labels from the detection head. While prior works mainly refine these pseudo-labels, they overlook the underlying need to strengthen the feature space itself. We propose FALCON-SFOD (Foundation-Aligned Learning with Clutter suppression and Noise robustness), a framework designed to enhance object-focused adaptation under domain shift. It consists of two complementary components. SPAR (Spatial Prior-Aware Regularization) leverages the generalization strength of vision foundation models to regularize the detector's feature space. Using class-agnostic binary masks derived from OV-SAM, SPAR promotes structured and foreground-focused activations by guiding the network toward object regions. IRPL (Imbalance-aware Noise Robust Pseudo-Labeling) complements SPAR by promoting balanced and noise-tolerant learning under severe foreground-background imbalance. Guided by a theoretical analysis that connects these designs to tighter localization and classification error bounds, FALCON-SFOD achieves competitive performance across SFOD benchmarks. |
| title | Foundation Model Priors Enhance Object Focus in Feature Space for Source-Free Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.17514 |