Nicolas Harvey Chapman — accepted AI-conference papers

2025

QueryAdapter: Rapid Adaptation of Vision-Language Models in Response to Natural Language Queries

Nicolas Harvey Chapman, Feras Dayoub, Will N. Browne, Christopher F. Lehnert

IROS 2025

A domain shift exists between the large-scale, internet data used to train a Vision-Language Model (VLM) and the raw image streams collected by a robot. Existing adaptation strategies require the definition of a closed-set of classes, which is impractical for a robot that must respond to diverse nat

Cited by 1 Source ↗Scholar ↗

2023

Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection

Nicolas Harvey Chapman, Feras Dayoub, Will N. Browne, Christopher F. Lehnert

RA-L 2023

Unsupervised Domain Adaptive Object Detection (UDA-OD) uses unlabelled data to improve the reliability of robotic vision systems in open-world environments. Previous approaches to UDA-OD based on self-training have been effective in overcoming changes in the general appearance of images. However, sh

Cited by 11 Source ↗code ↗Scholar ↗