# Underwater-Art: Expanding Information Perspectives With Text Templates For Underwater Acoustic Target Recognition

arXiv: https://arxiv.org/abs/2305.19612 · HTML: https://mlsanity.com/paper/2305.19612/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [AudioCLIP: Extending CLIP to Image, Text and Audio](https://mlsanity.com/paper/2106.13043/) (2021)
- [ESResNe(X)t-fbsp: Learning Robust Time-Frequency Transformation of Audio](https://mlsanity.com/paper/2104.11587/) (2021)

## Built on by (most cited first, 7 of 7)

- [Underwater Acoustic Target Recognition based on Smoothness-inducing Regularization and Spectrogram-based Data Augmentation](https://mlsanity.com/paper/2306.06945/) (2023)
- [Unraveling Complex Data Diversity in Underwater Acoustic Target Recognition through Convolution-based Mixture of Experts](https://mlsanity.com/paper/2402.11919/) (2024)
- [Oceanship: A Large-Scale Dataset for Underwater Audio Target Recognition](https://mlsanity.com/paper/2401.02099/) (2024)
- [Advancing Robust Underwater Acoustic Target Recognition through Multi-task Learning and Multi-Gate Mixture-of-Experts](https://mlsanity.com/paper/2411.02787/) (2024)
- [Adversarial multi-task underwater acoustic target recognition: towards robustness against various influential factors](https://mlsanity.com/paper/2411.02848/) (2024)
- [DEMONet: Underwater Acoustic Target Recognition based on Multi-Expert Network and Cross-Temporal Variational Autoencoder](https://mlsanity.com/paper/2411.02758/) (2024)
- [Graph Embedding with Mel-spectrograms for Underwater Acoustic Target Recognition](https://mlsanity.com/paper/2512.11545/) (2025)

## Abstract (arXiv)

Underwater acoustic target recognition is an intractable task due to the complex acoustic source characteristics and sound propagation patterns. Limited by insufficient data and narrow information perspective, recognition models based on deep learning seem far from satisfactory in practical underwater scenarios. Although underwater acoustic signals are severely influenced by distance, channel depth, or other factors, annotations of relevant information are often non-uniform, incomplete, and hard to use. In our work, we propose to implement Underwater Acoustic Recognition based on Templates made up of rich relevant information (hereinafter called "UART"). We design templates to integrate relevant information from different perspectives into descriptive natural language. UART adopts an audio-spectrogram-text tri-modal contrastive learning framework, which endows UART with the ability to guide the learning of acoustic representations by descriptive natural language. Our experiments reveal that UART has better recognition capability and generalization performance than traditional paradigms. Furthermore, the pre-trained UART model could provide superior prior knowledge for the recognition model in the scenario without any auxiliary annotation.