activity
20162023
most citedInverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image

16 citations · 61 across the 25 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CV20213 cited

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…

cs.CV20211 cited

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…

cs.CV2021

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…

cs.CV20211 cited

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…

cs.CV2021

Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation

Astuti Sharma, Tarun Kalluri, Manmohan Chandraker

Domain adaptation deals with training models using large scale labeled data from a specific source domain and then adapting the knowledge to certain target domains that have few or…

cs.CV2021

Cross-Domain Similarity Learning for Face Recognition in Unseen Domains

Masoud Faraki, Xiang Yu, Yi-Hsuan Tsai +2

Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as…