Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery

Autor:	Huynh, Andy V., Gillespie, Lauren E., Lopez-Saucedo, Jael, Tang, Claire, Sikand, Rohan, Expósito-Alonso, Moisés
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Multimodal image-text contrastive learning has shown that joint representations can be learned across modalities. Here, we show how leveraging multiple views of image data with contrastive learning can improve downstream fine-grained classification performance for species recognition, even when one view is absent. We propose ContRastive Image-remote Sensing Pre-training (CRISP)$\unicode{x2014}$a new pre-training task for ground-level and aerial image representation learning of the natural world$\unicode{x2014}$and introduce Nature Multi-View (NMV), a dataset of natural world imagery including $>3$ million ground-level and aerial image pairs for over 6,000 plant taxa across the ecologically diverse state of California. The NMV dataset and accompanying material are available at hf.co/datasets/andyvhuynh/NatureMultiView. Comment: Accepted to ECCV 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.19439 Zobrazit plný text záznamu View this record from Arxiv