Citation

Jahin, Md Abrar and Soudeep, Shahriar and Mridha, M. F. and Fahad, Nafiz and Hossen, Md. Jakir (2025) DyCAF-Net: Dynamic Class-Aware Fusion Network. In: 12th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2025, 9 October 2025 - 12 October 2025, Birmingham.

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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 equilibriumbased 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 (∼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 realworld detection tasks in medical imaging, surveillance, and autonomous systems. The code of DyCAF-Net is available at https://github.com/Abrar2652/DyCAF-NET.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Dynamic object detection, class-aware attention, multi-scale feature fusion,
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 05:13
Last Modified:	18 Mar 2026 06:49
URII:	http://shdl.mmu.edu.my/id/eprint/15552

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