Foundation Model Priors Enhance Object Focus in Feature Space for Source-Free Object Detection

• URL: http://arxiv.org/abs/2512.17514v2
• Date: Wed, 24 Dec 2025 07:10:56 GMT
• Title: Foundation Model Priors Enhance Object Focus in Feature Space for Source-Free Object Detection
• Authors: Sairam VCR, Rishabh Lalla, Aveen Dayal, Tejal Kulkarni, Anuj Lalla, Vineeth N Balasubramanian, Muhammad Haris Khan,
• Abstract summary: Domain shift reduces the detector's ability to maintain strong object-focused representations.<n>FALCON-SFOD is a framework designed to enhance object-focused adaptation under domain shift.
• Score: 38.14795337940857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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