29 citations · 30 across the 17 of their papers we have counts for
6 papers · 1 filter
Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection
Jiayi Zhou, David W. Johnston, Brinnae Bent
Despite the rapid uptake of black-box object detectors in marine mammal research and monitoring, explainability techniques are rarely integrated into conservation workflows. Furthe…
Explainable AI for Biodiversity Monitoring and Ecological Image Analysis
Brinnae Bent, Holly R. Houliston, Jiayi Zhou +2
Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, drones, satellites, underwater pla…
On Thin Ice: Towards Explainable Conservation Monitoring via Attribution and Perturbations
Jiayi Zhou, Günel Aghakishiyeva, Saagar Arya +7
Computer vision can accelerate ecological research and conservation monitoring, yet adoption in ecology lags in part because of a lack of trust in black-box neural-network-based mo…
Photorealistic Inpainting for Perturbation-based Explanations in Ecological Monitoring
Günel Aghakishiyeva, Jiayi Zhou, Saagar Arya +7
Ecological monitoring is increasingly automated by vision models, yet opaque predictions limit trust and field adoption. We present an inpainting-guided, perturbation-based explana…
Semantic Approach to Quantifying the Consistency of Diffusion Model Image Generation
Brinnae Bent
In this study, we identify the need for an interpretable, quantitative score of the repeatability, or consistency, of image generation in diffusion models. We propose a semantic ap…
InfiniteForm: A synthetic, minimal bias dataset for fitness applications
Andrew Weitz, Lina Colucci, Sidney Primas +1
The growing popularity of remote fitness has increased the demand for highly accurate computer vision models that track human poses. However, the best methods still fail in many re…