Shivani Chiranjeevi

dblp:346/0518 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Image recognition and object detection · 87% Vision and language · 13%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Environmental and earth informatics · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › image classification
fine-grained image classification
0.812024
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity · NeurIPS 2024
Computer vision › Image recognition and object detection › image classification › fine-grained image classification
species recognition
0.812024
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity · NeurIPS 2024
Environmental and earth informatics
biodiversity informatics
0.812024
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity · NeurIPS 2024
Computer vision › Vision and language
vision-language pretraining
0.212024
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity · NeurIPS 2024

Methods — techniques the papers use, named apart from their topics

zero-shot learning · 1.5CLIP · 1.5
YearPublicationVenuePosition
2024 BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
abstract
We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity. Curated from the iNaturalist platform and vetted to include only research-grade data, BioTrove contains 161.9 million images, offering unprecedented scale and diversity from three primary kingdoms: Animalia ("animals"), Fungi ("fungi"), and Plantae ("plants"), spanning approximately 366.6K species. Each image is annotated with scientific names, taxonomic hierarchies, and common names, providing rich metadata to support accurate AI model development across diverse species and ecosystems.We demonstrate the value of BioTrove by releasing a suite of CLIP models trained using a subset of 40 million captioned images, known as BioTrove-Train. This subset focuses on seven categories within the dataset that are underrepresented in standard image recognition models, selected for their critical role in biodiversity and agriculture: Aves ("birds"), Arachnida} ("spiders/ticks/mites"), Insecta ("insects"), Plantae ("plants"), Fungi ("fungi"), Mollusca ("snails"), and Reptilia ("snakes/lizards"). To support rigorous assessment, we introduce several new benchmarks and report model accuracy for zero-shot learning across life stages, rare species, confounding species, and multiple taxonomic levels.We anticipate that BioTrove will spur the development of AI models capable of supporting digital tools for pest control, crop monitoring, biodiversity assessment, and environmental conservation. These advancements are crucial for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. BioTrove is publicly available, easily accessible, and ready for immediate use.
Chih-Hsuan Yang, Benjamin Feuer, Talukder Z. Jubery, Zi K. Deng, Andre Nakkab, Md. Zahid Hasan, Shivani Chiranjeevi, Kelly O. Marshall, Nirmal Baishnab, Asheesh Kumar Singh, Arti Singh, Soumik Sarkar, Nirav C. Merchant, Chinmay Hegde, Baskar Ganapathysubramanian
NeurIPS7