16 citations · 59 across the 18 of their papers we have counts for
41 papers · 1 filter
Controllable Dynamic Multi-Task Architectures
Dripta S. Raychaudhuri, Yumin Suh, Samuel Schulter +4
Multi-task learning commonly encounters competition for resources among tasks, specifically when model capacity is limited. This challenge motivates models which allow control over…
Learning Semantic Segmentation from Multiple Datasets with Label Shifts
Dongwan Kim, Yi-Hsuan Tsai, Yumin Suh +4
With increasing applications of semantic segmentation, numerous datasets have been proposed in the past few years. Yet labeling remains expensive, thus, it is desirable to jointly…
Learning Cross-modal Contrastive Features for Video Domain Adaptation
Donghyun Kim, Yi-Hsuan Tsai, Bingbing Zhuang +4
Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition. Existing video domain adaptation met…
Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty
Bingbing Zhuang, Manmohan Chandraker
Learning methods for relative camera pose estimation have been developed largely in isolation from classical geometric approaches. The question of how to integrate predictions from…
Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction
Sriram Narayanan, Ramin Moslemi, Francesco Pittaluga +2
Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multim…
Modulated Periodic Activations for Generalizable Local Functional Representations
Ishit Mehta, Michaël Gharbi, Connelly Barnes +3
Multi-Layer Perceptrons (MLPs) make powerful functional representations for sampling and reconstruction problems involving low-dimensional signals like images,shapes and light fiel…