26 citations · 28 across the 3 of their papers we have counts for
4 papers · 1 filter
We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline
Simar Kareer, Vivek Vijaykumar, Harsh Maheshwari +3
There has been abundant work in unsupervised domain adaptation for semantic segmentation (DAS) seeking to adapt a model trained on images from a labeled source domain to an unlabel…
SkyScenes: A Synthetic Dataset for Aerial Scene Understanding
Sahil Khose, Anisha Pal, Aayushi Agarwal +3
Real-world aerial scene understanding is limited by a lack of datasets that contain densely annotated images curated under a diverse set of conditions. Due to inherent challenges i…
AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Images
Prithvijit Chattopadhyay, Bharat Goyal, Boglarka Ecsedi +2
Synthetic data (SIM) drawn from simulators have emerged as a popular alternative for training models where acquiring annotated real-world images is difficult. However, transferring…
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
Micah Goldblum, Hossein Souri, Renkun Ni +10
Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an…