no code implementations • 8 May 2024 • Prannay Kaul, Zhizhong Li, Hao Yang, Yonatan Dukler, Ashwin Swaminathan, C. J. Taylor, Stefano Soatto
By evaluating a large selection of recent LVLMs using public datasets, we show that an improvement in existing metrics do not lead to a reduction in Type I hallucinations, and that established benchmarks for measuring Type I hallucinations are incomplete.
no code implementations • 8 Jun 2023 • Prannay Kaul, Weidi Xie, Andrew Zisserman
The goal of this paper is open-vocabulary object detection (OVOD) $\unicode{x2013}$ building a model that can detect objects beyond the set of categories seen at training, thus enabling the user to specify categories of interest at inference without the need for model retraining.
1 code implementation • CVPR 2022 • Prannay Kaul, Weidi Xie, Andrew Zisserman
The objective of this paper is few-shot object detection (FSOD) -- the task of expanding an object detector for a new category given only a few instances for training.
no code implementations • 2 Apr 2020 • Prannay Kaul, Daniele De Martini, Matthew Gadd, Paul Newman
This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using FMCW scanning radar.