Litu Rout

TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

TL;DR: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative; results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

TL;DR: In this paper , the authors provide a theoretical justification for sample recovery using diffusion based image inpainting in a linear model setting, and propose a modified RePaint algorithm called RePaints$^+$ that provably recovers the underlying true sample and enjoys a linear rate of convergence.

TL;DR: The Visual Object Tracking challenge VOT2022 as mentioned in this paper was composed of seven sub-challenges focusing on different tracking domains: (i) VOT-STs2022 challenge focused on short-term tracking in RGB by segmentation, (ii) VOTE-STb2022 challenging was focused on real-time short-time tracking by bounding boxes, (iii) VODE-RTb2021 challenge was concerned with segmentation of RGB and depth-only images, and (iv) as mentioned in this paper focused on long-term longterm tracking by coping with target disappearance and reappearance.

TL;DR: An algorithm is proposed for unpaired SR which learns an unbiased OT map for the perceptual transport cost and provides nearly state-of-the-art performance on the large-scale unpaired AIM-19 dataset.