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pro vyhledávání: '"Jesslen, Artur"'
Autor:
Fischer, Tom, Liu, Yaoyao, Jesslen, Artur, Ahmed, Noor, Kaushik, Prakhar, Wang, Angtian, Yuille, Alan, Kortylewski, Adam, Ilg, Eddy
Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have recently addressed handling continual data streams. However, extending th
Externí odkaz:
http://arxiv.org/abs/2407.09271
Publikováno v:
Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22787-22796
Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics, e.g. for embodied agents or to train 3D generative models. However, so far methods that estimate the category-level object pose require either lar
Externí odkaz:
http://arxiv.org/abs/2407.04384
Autor:
Xu, Jiacong, Zhang, Yi, Peng, Jiawei, Ma, Wufei, Jesslen, Artur, Ji, Pengliang, Hu, Qixin, Zhang, Jiehua, Liu, Qihao, Wang, Jiahao, Ji, Wei, Wang, Chen, Yuan, Xiaoding, Kaushik, Prakhar, Zhang, Guofeng, Liu, Jie, Xie, Yushan, Cui, Yawen, Yuille, Alan, Kortylewski, Adam
Accurately estimating the 3D pose and shape is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife conservation. However, research in this area is held back by the lack o
Externí odkaz:
http://arxiv.org/abs/2308.11737
Discriminative models for object classification typically learn image-based representations that do not capture the compositional and 3D nature of objects. In this work, we show that explicitly integrating 3D compositional object representations into
Externí odkaz:
http://arxiv.org/abs/2305.14668
Autor:
Zhao, Bingchen, Wang, Jiahao, Ma, Wufei, Jesslen, Artur, Yang, Siwei, Yu, Shaozuo, Zendel, Oliver, Theobalt, Christian, Yuille, Alan, Kortylewski, Adam
Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introd
Externí odkaz:
http://arxiv.org/abs/2304.10266
In real-world applications, it is essential to jointly estimate the 3D object pose and class label of objects, i.e., to perform 3D-aware classification.While current approaches for either image classification or pose estimation can be extended to 3D-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce1ed1ea3a335a09380197c6d2442d4d
http://arxiv.org/abs/2305.14668
http://arxiv.org/abs/2305.14668