6 citations · 20 across the 16 of their papers we have counts for
4 papers · 1 filter
Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation
Muzhi Zhu, Yang Liu, Zekai Luo +5
The Diffusion Model has not only garnered noteworthy achievements in the realm of image generation but has also demonstrated its potential as an effective pretraining method utiliz…
GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation Models
Yongtao Ge, Guangkai Xu, Zhiyue Zhao +5
Recent advances in discriminative and generative pretraining have yielded geometry estimation models with strong generalization capabilities. While discriminative monocular geometr…
DiffCalib: Reformulating Monocular Camera Calibration as Diffusion-Based Dense Incident Map Generation
Xiankang He, Guangkai Xu, Bo Zhang +3
Monocular camera calibration is a key precondition for numerous 3D vision applications. Despite considerable advancements, existing methods often hinge on specific assumptions and…
What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?
Guangkai Xu, Yongtao Ge, Mingyu Liu +5
Extensive pre-training with large data is indispensable for downstream geometry and semantic visual perception tasks. Thanks to large-scale text-to-image (T2I) pretraining, recent…