DyCAF-Net: Dynamic Class-Aware Fusion Network

• URL: http://arxiv.org/abs/2508.03598v1
• Date: Tue, 05 Aug 2025 16:06:26 GMT
• Title: DyCAF-Net: Dynamic Class-Aware Fusion Network
• Authors: Md Abrar Jahin, Shahriar Soudeep, M. F. Mridha, Nafiz Fahad, Md. Jakir Hossen,
• Abstract summary: We introduce Dynamic Class-Aware Fusion Network (DyCAF-Net)<n>DyCAF-Net achieves significant improvements in precision, mAP@50, and mAP@50-95 across 13 diverse benchmarks.<n>Its adaptability to scale variance, semantic overlaps, and class imbalance positions it as a robust solution for real-world detection tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in object detection rely on modular architectures with multi-scale fusion and attention mechanisms. However, static fusion heuristics and class-agnostic attention limit performance in dynamic scenes with occlusions, clutter, and class imbalance. We introduce Dynamic Class-Aware Fusion Network (DyCAF-Net) that addresses these challenges through three innovations: (1) an input-conditioned equilibrium-based neck that iteratively refines multi-scale features via implicit fixed-point modeling, (2) a dual dynamic attention mechanism that adaptively recalibrates channel and spatial responses using input- and class-dependent cues, and (3) class-aware feature adaptation that modulates features to prioritize discriminative regions for rare classes. Through comprehensive ablation studies with YOLOv8 and related architectures, alongside benchmarking against nine state-of-the-art baselines, DyCAF-Net achieves significant improvements in precision, mAP@50, and mAP@50-95 across 13 diverse benchmarks, including occlusion-heavy and long-tailed datasets. The framework maintains computational efficiency ($\sim$11.1M parameters) and competitive inference speeds, while its adaptability to scale variance, semantic overlaps, and class imbalance positions it as a robust solution for real-world detection tasks in medical imaging, surveillance, and autonomous systems.

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