Publications (4)
ChiMera: Learning with noisy labels by contrasting mixed-up augmentations
Zixuan Liu, Xin Zhang, Junjun He +5
Learning with noisy labels has been studied to address incorrect label annotations in real-world applications. In this paper, we present ChiMera, a two-stage learning-from-noisy-la…
Seeing Beyond Haze: Generative Nighttime Image Dehazing
Beibei Lin, Stephen Lin, Robby Tan
Nighttime image dehazing is particularly challenging when dense haze and intense glow severely degrade or entirely obscure background information. Existing methods often struggle d…
NightRain: Nighttime Video Deraining via Adaptive-Rain-Removal and Adaptive-Correction
Beibei Lin, Yeying Jin, Wending Yan +4
Existing deep-learning-based methods for nighttime video deraining rely on synthetic data due to the absence of real-world paired data. However, the intricacies of the real world,…
Continuous-time Radar-inertial Odometry for Automotive Radars
Yin Zhi Ng, Benjamin Choi, Robby Tan +1
We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU…