5 papers · 1 filter
Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation
Volodymyr Havrylov, Haiwen Huang, Dan Zhang +1
Vision Foundation Models (VFMs) are large-scale, pre-trained models that serve as general-purpose backbones for various computer vision tasks. As VFMs' popularity grows, there is a…
LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models
Haiwen Huang, Anpei Chen, Volodymyr Havrylov +2
Vision foundation models (VFMs) such as DINOv2 and CLIP have achieved impressive results on various downstream tasks, but their limited feature resolution hampers performance in ap…
UNCOVER: Unknown Class Object Detection for Autonomous Vehicles in Real-time
Lars Schmarje, Kaspar Sakman, Reinhard Koch +1
Autonomous driving (AD) operates in open-world scenarios, where encountering unknown objects is inevitable. However, standard object detectors trained on a limited number of base c…
Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models
Peng Cui, Guande He, Dan Zhang +3
Datasets collected from the open world unavoidably suffer from various forms of randomness or noiseness, leading to the ubiquity of aleatoric (data) uncertainty. Quantifying such u…
Renovating Names in Open-Vocabulary Segmentation Benchmarks
Haiwen Huang, Songyou Peng, Dan Zhang +1
Names are essential to both human cognition and vision-language models. Open-vocabulary models utilize class names as text prompts to generalize to categories unseen during trainin…