6 papers
Identifying and Resolving Pitfalls of Knowledge-Based VQA Benchmarks: Auditing, Repairing, and Augmenting
Qian Ma, S M Rayeed, Charles V. Stewart +2
Knowledge-Based Visual Question Answering (KB-VQA) aims to evaluate whether Visual Language Models (VLMs) can retrieve, ground, and reason over external structured knowledge beyond…
BeetleFlow: An Integrative Deep Learning Pipeline for Beetle Image Processing
Fangxun Liu, S M Rayeed, Samuel Stevens +21
In entomology and ecology research, biologists often need to collect a large number of insects, among which beetles are the most common species. A common practice for biologists to…
BeetleVerse: A Study on Taxonomic Classification of Ground Beetles
S M Rayeed, Alyson East, Samuel Stevens +2
Ground beetles are a highly sensitive and speciose biological indicator, making them vital for monitoring biodiversity. However, they are currently an underutilized resource due to…
A continental-scale dataset of ground beetles with high-resolution images and validated morphological trait measurements
S M Rayeed, Mridul Khurana, Alyson East +18
Despite the ecological significance of invertebrates, global trait databases remain heavily biased toward vertebrates and plants, limiting comprehensive ecological analyses of high…
Mind the (Data) Gap: Evaluating Vision Systems in Small Data Applications
Samuel Stevens, S M Rayeed, Jenna Kline
The practical application of AI tools for specific computer vision tasks relies on the "small-data regime" of hundreds to thousands of labeled samples. This small-data regime is vi…
Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens
Alyson East, Elizabeth G. Campolongo, Luke Meyers +25
1) Biological collections house millions of specimens with digital images increasingly available through open-access platforms. However, most imaging protocols were developed for h…