# FishAI 2.0: Marine Fish Image Classification with Multi-modal Few-shot Learning

arXiv: https://arxiv.org/abs/2509.22930 · HTML: https://mlsanity.com/paper/2509.22930/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 26 Sep 2025.

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

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Diagnosing and Enhancing VAE Models](https://mlsanity.com/paper/1903.05789/) (2019)
- [Comparative Analysis of OpenAI GPT-4o and DeepSeek R1 for Scientific Text Categorization Using Prompt Engineering](https://mlsanity.com/paper/2503.02032/) (2025)
- [FLIER: Few-shot Language Image Models Embedded with Latent Representations](https://mlsanity.com/paper/2410.07648/) (2024)

## Abstract (arXiv)

Traditional marine biological image recognition faces challenges of incomplete datasets and unsatisfactory model accuracy, particularly for few-shot conditions of rare species where data scarcity significantly hampers the performance. To address these issues, this study proposes an intelligent marine fish recognition framework, FishAI 2.0, integrating multimodal few-shot deep learning techniques with image generation for data augmentation. First, a hierarchical marine fish benchmark dataset, which provides a comprehensive data foundation for subsequent model training, is utilized to train the FishAI 2.0 model. To address the data scarcity of rare classes, the large language model DeepSeek was employed to generate high-quality textual descriptions, which are input into Stable Diffusion 2 for image augmentation through a hierarchical diffusion strategy that extracts latent encoding to construct a multimodal feature space. The enhanced visual-textual datasets were then fed into a Contrastive Language-Image Pre-Training (CLIP) based model, enabling robust few-shot image recognition. Experimental results demonstrate that FishAI 2.0 achieves a Top-1 accuracy of 91.67 percent and Top-5 accuracy of 97.97 percent at the family level, outperforming baseline CLIP and ViT models with a substantial margin for the minority classes with fewer than 10 training samples. To better apply FishAI 2.0 to real-world scenarios, at the genus and species level, FishAI 2.0 respectively achieves a Top-1 accuracy of 87.58 percent and 85.42 percent, demonstrating practical utility. In summary, FishAI 2.0 improves the efficiency and accuracy of marine fish identification and provides a scalable technical solution for marine ecological monitoring and conservation, highlighting its scientific value and practical applicability.