# CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic Recognition

arXiv: https://arxiv.org/abs/2501.09877 · HTML: https://mlsanity.com/paper/2501.09877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling](https://mlsanity.com/paper/2111.03930/) (2021)
- [Natural Language Supervision for General-Purpose Audio Representations](https://mlsanity.com/paper/2309.05767/) (2023)
- [Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking](https://mlsanity.com/paper/2406.16148/) (2024)
- [An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2107.13530/) (2021)
- [ResMem: Learn what you can and memorize the rest](https://mlsanity.com/paper/2302.01576/) (2023)
- [Audio-free Prompt Tuning for Language-Audio Models](https://mlsanity.com/paper/2309.08357/) (2023)
- [Prompt Tuning based Adapter for Vision-Language Model Adaption](https://mlsanity.com/paper/2303.15234/) (2023)
- [Adapting Language-Audio Models as Few-Shot Audio Learners](https://mlsanity.com/paper/2305.17719/) (2023)
- [PALM: Few-Shot Prompt Learning for Audio Language Models](https://mlsanity.com/paper/2409.19806/) (2024)
- [Low-rank constrained multichannel signal denoising considering channel-dependent sensitivity inspired by self-supervised learning for optical fiber sensing](https://mlsanity.com/paper/2312.08660/) (2023)

## Built on by (most cited first, 1 of 1)

- [Drift-Augmented Scoring: Text-Derived Noise Robustness for Zero-Shot Audio-Language Classification](https://mlsanity.com/paper/2606.04844/) (2026)

## Abstract (arXiv)

Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber-optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiber-optic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory-recorded fiber-optic ESC-50 datasets and a real-world fiber-optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks. The code and gunshot-firework dataset are available at https://github.com/Jingchensun/clap-s.