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Instance-level feature representation calibration for visual object detection
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AI
DOI：10.1016/j.displa.2025.103130.png)
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Abstract
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• The Highlights of this paper are summarized as follows: Prototype-Based Supervised Contrastive Learning: We introduced a novel prototype-based supervised contrastive learning method to reduce representation bias, enhancing the discriminative power of features for more accurate few-shot object detection. • Balanced Cross-Entropy Loss Function: We developed a balanced cross-entropy loss function that ensures stable detector performance, even with unbalanced sample sizes and limited data for novel classes, overcoming challenges in few-shot learning. • Experimental Validation on Benchmark Datasets: Extensive experiments on PASCAL VOC and MS-COCO benchmarks demonstrate the superior performance and effectiveness of our method compared to existing approaches.
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