# FishNet++: Analyzing the capabilities of Multimodal Large Language Models in marine biology

arXiv: https://arxiv.org/abs/2509.25564 · HTML: https://mlsanity.com/paper/2509.25564/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agricultural vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 30 of its 83 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Agricultural vision-language models](https://mlsanity.com/mllm/agricultural-vision-language-models/)

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning](https://mlsanity.com/paper/2310.09478/) (2023)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text](https://mlsanity.com/paper/2210.02928/) (2022)
- [Pixtral 12B](https://mlsanity.com/paper/2410.07073/) (2024)
- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries](https://mlsanity.com/paper/2106.09178/) (2021)
- [AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish](https://mlsanity.com/paper/2501.03767/) (2025)

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

- [ShellfishNet: A Domain-Specific Benchmark for Visual Recognition of Marine Molluscs](https://mlsanity.com/paper/2605.07338/) (2026)

## Abstract (arXiv)

Multimodal large language models (MLLMs) have demonstrated impressive cross-domain capabilities, yet their proficiency in specialized scientific fields like marine biology remains underexplored. In this work, we systematically evaluate state-of-the-art MLLMs and reveal significant limitations in their ability to perform fine-grained recognition of fish species, with the best open-source models achieving less than 10% accuracy. This task is critical for monitoring marine ecosystems under anthropogenic pressure. To address this gap and investigate whether these failures stem from a lack of domain knowledge, we introduce FishNet++, a large-scale, multimodal benchmark. FishNet++ significantly extends existing resources with 35,133 textual descriptions for multimodal learning, 706,426 key-point annotations for morphological studies, and 119,399 bounding boxes for detection. By providing this comprehensive suite of annotations, our work facilitates the development and evaluation of specialized vision-language models capable of advancing aquatic science.