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pro vyhledávání: '"Gurumurthy, Swaminathan"'
Various pose estimation and tracking problems in robotics can be decomposed into a correspondence estimation problem (often computed using a deep network) followed by a weighted least squares optimization problem to solve for the poses. Recent work h
Externí odkaz:
http://arxiv.org/abs/2406.07785
Publikováno v:
Neurips 2021
Many tasks in deep learning involve optimizing over the \emph{inputs} to a network to minimize or maximize some objective; examples include optimization over latent spaces in a generative model to match a target image, or adversarially perturbing an
Externí odkaz:
http://arxiv.org/abs/2111.13236
Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples. Developing efficient exploration strategies capable of finding the most useful samples become
Externí odkaz:
http://arxiv.org/abs/1911.04024
The task of conducting visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers in natural language. We posit that req
Externí odkaz:
http://arxiv.org/abs/1808.04359
Semantic shape completion is a challenging problem in 3D computer vision where the task is to generate a complete 3D shape using a partial 3D shape as input. We propose a learning-based approach to complete incomplete 3D shapes through generative mod
Externí odkaz:
http://arxiv.org/abs/1807.03407
Autor:
Gurumurthy, Swaminathan, Yu, Lantao, Zhang, Chenyan, Jin, Yongchao, Li, Weiping, Zhang, Haidong, Fang, Fei
Poaching continues to be a significant threat to the conservation of wildlife and the associated ecosystem. Estimating and predicting where the poachers have committed or would commit crimes is essential to more effective allocation of patrolling res
Externí odkaz:
http://arxiv.org/abs/1805.05356
A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typi
Externí odkaz:
http://arxiv.org/abs/1706.02071
Akademický článek
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Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6920e1caaa8c1760aad2a7e40e39473a
https://doi.org/10.1109/wacv56688.2023.00226
https://doi.org/10.1109/wacv56688.2023.00226
Autor:
Pei Wang, Zhaowei Cai, Hao Yang, Gurumurthy Swaminathan, Nuno Vasconcelos, Bernt Schiele, Stefano Soatto
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition
We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for object detection. This is enabled by a unified architecture, Omni-DETR,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cede7892bcd7ba37b64ba23cc705473a
http://arxiv.org/abs/2203.16089
http://arxiv.org/abs/2203.16089