# A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition

arXiv: https://arxiv.org/abs/2504.13102 · HTML: https://mlsanity.com/paper/2504.13102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SimpleNet: A Simple Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2303.15140/) (2023)
- [CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://mlsanity.com/paper/2303.00332/) (2023)
- [An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification](https://mlsanity.com/paper/2305.12838/) (2023)
- [Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization](https://mlsanity.com/paper/2103.01499/) (2021)
- [SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization](https://mlsanity.com/paper/2405.11582/) (2024)
- [Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems](https://mlsanity.com/paper/1904.03476/) (2019)
- [A Multi-Task Learning Framework for Sound Event Detection using High-level Acoustic Characteristics of Sounds](https://mlsanity.com/paper/2305.10729/) (2023)
- [Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition](https://mlsanity.com/paper/1701.02477/) (2017)
- [ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection](https://mlsanity.com/paper/1906.07912/) (2019)

## Abstract (arXiv)

Underwater acoustic target recognition (UATR) is of great significance for the protection of marine diversity and national defense security. The development of deep learning provides new opportunities for UATR, but faces challenges brought by the scarcity of reference samples and complex environmental interference. To address these issues, we proposes a multi-task balanced channel attention convolutional neural network (MT-BCA-CNN). The method integrates a channel attention mechanism with a multi-task learning strategy, constructing a shared feature extractor and multi-task classifiers to jointly optimize target classification and feature reconstruction tasks. The channel attention mechanism dynamically enhances discriminative acoustic features such as harmonic structures while suppressing noise. Experiments on the Watkins Marine Life Dataset demonstrate that MT-BCA-CNN achieves 97% classification accuracy and 95% F1-score in 27-class few-shot scenarios, significantly outperforming traditional CNN and ACNN models, as well as popular state-of-the-art UATR methods. Ablation studies confirm the synergistic benefits of multi-task learning and attention mechanisms, while a dynamic weighting adjustment strategy effectively balances task contributions. This work provides an efficient solution for few-shot underwater acoustic recognition, advancing research in marine bioacoustics and sonar signal processing.