Paper Reading AI Learner

Insect Identification in the Wild: The AMI Dataset

2024-06-18 09:57:02
Aditya Jain, Fagner Cunha, Michael James Bunsen, Juan Sebasti\'an Ca\~nas, L\'eonard Pasi, Nathan Pinoy, Flemming Helsing, JoAnne Russo, Marc Botham, Michael Sabourin, Jonathan Fr\'echette, Alexandre Anctil, Yacksecari Lopez, Eduardo Navarro, Filonila Perez Pimentel, Ana Cecilia Zamora, Jos\'e Alejandro Ramirez Silva, Jonathan Gagnon, Tom August, Kim Bjerge, Alba Gomez Segura, Marc B\'elisle, Yves Basset, Kent P. McFarland, David Roy, Toke Thomas H{\o}ye, Maxim Larriv\'ee, David Rolnick


Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups. Code and datasets are made publicly available.

Abstract (translated)

昆虫是所有全球生物多样性的一半,然而世界范围内很多昆虫正在消失,对生态系统和农业造成了严重影响。尽管如此,昆虫多样性和丰度数据仍然非常不足,由于人类专家的稀缺和可扩展的数据监测工具的缺乏。生态学家开始使用相机陷阱记录和研究昆虫,并提出了计算机视觉算法作为可扩展数据处理的答案。然而,在计算机视觉中,野生昆虫监测面临着独特的挑战,包括长尾数据的组合、极其相似的类和显著的分布变化。我们为生态学家提供了第一个大型的机器学习基准,针对细粒度昆虫识别,以匹配生态学家面临的真实世界任务。我们的贡献包括一个由公民科学平台和博物馆的图片组成的 curated数据集,以及一个来自多个大陆的自动相机陷阱的专家注释数据集,旨在在实地条件下测试离散分布的泛化。我们训练和评估了各种基线算法,并引入了一种数据增强技术,旨在增强在地理和硬件环境中的泛化。代码和数据集都是公开可用的。



3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot