# Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding

arXiv: https://arxiv.org/abs/2311.15206 · HTML: https://mlsanity.com/paper/2311.15206/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 75 references that are in the map.
- First posted on arXiv 26 Nov 2023.

## Builds on (most cited first, 30 of 51)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)

## Built on by (most cited first, 29 of 29)

- [BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity](https://mlsanity.com/paper/2406.12723/) (2024)
- [LEMON: A Large Endoscopic MONocular Dataset and Foundation Model for Perception in Surgical Settings](https://mlsanity.com/paper/2503.19740/) (2025)
- [CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale](https://mlsanity.com/paper/2405.17537/) (2024)
- [QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering](https://mlsanity.com/paper/2405.19722/) (2024)
- [Quantum Visual Feature Encoding Revisited](https://mlsanity.com/paper/2405.19725/) (2024)
- [Hierarchical Quantum Control Gates for Functional MRI Understanding](https://mlsanity.com/paper/2408.03596/) (2024)
- [FungiTastic: A multi-modal dataset and benchmark for image categorization](https://mlsanity.com/paper/2408.13632/) (2024)
- [BRACTIVE: A Brain Activation Approach to Human Visual Brain Learning](https://mlsanity.com/paper/2405.18808/) (2024)
- [EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding](https://mlsanity.com/paper/2406.01429/) (2024)
- [Directed-Tokens: A Robust Multi-Modality Alignment Approach to Large Language-Vision Models](https://mlsanity.com/paper/2508.14264/) (2025)
- [COBRA: A Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2411.17475/) (2024)
- [Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI](https://mlsanity.com/paper/2312.00236/) (2023)
- [RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition](https://mlsanity.com/paper/2410.12718/) (2024)
- [Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Traps](https://mlsanity.com/paper/2411.14467/) (2024)
- [Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring](https://mlsanity.com/paper/2503.01691/) (2025)
- [MANGO: Multimodal Attention-based Normalizing Flow Approach to Fusion Learning](https://mlsanity.com/paper/2508.10133/) (2025)
- [Video-Based Autism Detection with Deep Learning](https://mlsanity.com/paper/2402.16774/) (2024)
- [BeetleVerse: A Study on Taxonomic Classification of Ground Beetles](https://mlsanity.com/paper/2504.13393/) (2025)
- [φ-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models](https://mlsanity.com/paper/2602.22601/) (2026)
- [Deep-Wide Learning Assistance for Insect Pest Classification](https://mlsanity.com/paper/2409.10445/) (2024)
- [A Novel Dataset for Video-Based Neurodivergent Classification Leveraging Extra-Stimulatory Behavior](https://mlsanity.com/paper/2409.04598/) (2024)
- [AquaMonitor: A multimodal multi-view image sequence dataset for real-life aquatic invertebrate biodiversity monitoring](https://mlsanity.com/paper/2505.22065/) (2025)
- [BioAnalyst: A Foundation Model for Biodiversity](https://mlsanity.com/paper/2507.09080/) (2025)
- [BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2508.18187/) (2025)
- [Bridging Domain Gaps for Fine-Grained Moth Classification Through Expert-Informed Adaptation and Foundation Model Priors](https://mlsanity.com/paper/2508.20089/) (2025)
- [BioAutoML-NAS: An End-to-End AutoML Framework for Multimodal Insect Classification via Neural Architecture Search on Large-Scale Biodiversity Data](https://mlsanity.com/paper/2510.05888/) (2025)
- [Hard Samples, Bad Labels: Robust Loss Functions That Know When to Back Off](https://mlsanity.com/paper/2511.16512/) (2025)
- [PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction](https://mlsanity.com/paper/2604.17278/) (2026)
- [α-Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modeling](https://mlsanity.com/paper/2609.07961/) (2026)

## Abstract (arXiv)

In precision agriculture, the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield. The current machine vision model requires a large volume of data to achieve high performance. However, there are approximately 5.5 million different insect species in the world. None of the existing insect datasets can cover even a fraction of them due to varying geographic locations and acquisition costs. In this paper, we introduce a novel "Insect-1M" dataset, a game-changing resource poised to revolutionize insect-related foundation model training. Covering a vast spectrum of insect species, our dataset, including 1 million images with dense identification labels of taxonomy hierarchy and insect descriptions, offers a panoramic view of entomology, enabling foundation models to comprehend visual and semantic information about insects like never before. Then, to efficiently establish an Insect Foundation Model, we develop a micro-feature self-supervised learning method with a Patch-wise Relevant Attention mechanism capable of discerning the subtle differences among insect images. In addition, we introduce Description Consistency loss to improve micro-feature modeling via insect descriptions. Through our experiments, we illustrate the effectiveness of our proposed approach in insect modeling and achieve State-of-the-Art performance on standard benchmarks of insect-related tasks. Our Insect Foundation Model and Dataset promise to empower the next generation of insect-related vision models, bringing them closer to the ultimate goal of precision agriculture.