activity
20232026
most citedBattle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks

26 citations · 29 across the 5 of their papers we have counts for

collaborators

5 papers

physics.ao-ph2026

Video Diffusion for Satellite-based High-Dynamical-Fidelity Precipitation (HiDFiP) Field Generation

Runze Li, Yan Xia, Yongquan Qu +6

High spatiotemporal fidelity precipitation products that accurately capture storm spatial organization, propagation, and lifecycle evolution, are essential for advancing hydrometeo…

cs.CV2024★ 2 cited

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…

cs.CV2023

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…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 26 cited

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…