no code implementations • EMNLP (BlackboxNLP) 2021 • Federico Fancellu, Lan Xiao, Allan Jepson, Afsaneh Fazly
We address this gap for two structure generation tasks, namely dependency and semantic parsing.
no code implementations • 13 Apr 2024 • Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson
One fundamental operation applied to such representations is differentiable rendering, as it enables inverse graphics approaches in learning frameworks.
no code implementations • 25 Jan 2023 • Stavros Tsogkas, Fengjia Zhang, Allan Jepson, Alex Levinshtein
Taking photographs ''in-the-wild'' is often hindered by fence obstructions that stand between the camera user and the scene of interest, and which are hard or impossible to avoid.
no code implementations • 20 Apr 2022 • Leila Pishdad, Ran Zhang, Konstantinos G. Derpanis, Allan Jepson, Afsaneh Fazly
Probabilistic embeddings have proven useful for capturing polysemous word meanings, as well as ambiguity in image matching.
1 code implementation • 6 Feb 2022 • Mete Kemertas, Allan Jepson
Based on these results, we design an API($\alpha$) procedure that employs conservative policy updates and enjoys better performance bounds than the naive API approach.
no code implementations • CVPR 2022 • Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson
On the other hand, implicit representations (occupancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability.
no code implementations • 4 Nov 2021 • Vineeth S. Bhaskara, Tristan Aumentado-Armstrong, Allan Jepson, Alex Levinshtein
Under such a class of discriminator (or critic) functions, we present Gradient Normalization (GraN), a novel input-dependent normalization method, which guarantees a piecewise K-Lipschitz constraint in the input space.
1 code implementation • 31 May 2021 • Maayan Shvo, Zhiming Hu, Rodrigo Toro Icarte, Iqbal Mohomed, Allan Jepson, Sheila A. McIlraith
We introduce an RL-based framework for learning to accomplish tasks in mobile apps.
no code implementations • EACL 2021 • {\'A}kos K{\'a}d{\'a}r, Lan Xiao, Mete Kemertas, Federico Fancellu, Allan Jepson, Afsaneh Fazly
We do so by casting dependency parsing as a tree embedding problem where we incorporate geometric properties of dependency trees in the form of training losses within a graph-based parser.
no code implementations • 27 Feb 2021 • Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson
In this work, we improve on a prior generative model of geometric disentanglement for 3D shapes, wherein the space of object geometry is factorized into rigid orientation, non-rigid pose, and intrinsic shape.
no code implementations • 1 Jan 2021 • Leila Pishdad, Ran Zhang, Afsaneh Fazly, Allan Jepson
Learning multimodal representations is a requirement for many tasks such as image--caption retrieval.
3 code implementations • 15 Sep 2020 • Kai Zhang, Martin Danelljan, Yawei Li, Radu Timofte, Jie Liu, Jie Tang, Gangshan Wu, Yu Zhu, Xiangyu He, Wenjie Xu, Chenghua Li, Cong Leng, Jian Cheng, Guangyang Wu, Wenyi Wang, Xiaohong Liu, Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong, Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan, Xiaochuan Li, Zhiqiang Lang, Jiangtao Nie, Wei Wei, Lei Zhang, Abdul Muqeet, Jiwon Hwang, Subin Yang, JungHeum Kang, Sung-Ho Bae, Yongwoo Kim, Geun-Woo Jeon, Jun-Ho Choi, Jun-Hyuk Kim, Jong-Seok Lee, Steven Marty, Eric Marty, Dongliang Xiong, Siang Chen, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Haicheng Wang, Vineeth Bhaskara, Alex Levinshtein, Stavros Tsogkas, Allan Jepson, Xiangzhen Kong, Tongtong Zhao, Shanshan Zhao, Hrishikesh P. S, Densen Puthussery, Jiji C. V, Nan Nan, Shuai Liu, Jie Cai, Zibo Meng, Jiaming Ding, Chiu Man Ho, Xuehui Wang, Qiong Yan, Yuzhi Zhao, Long Chen, Jiangtao Zhang, Xiaotong Luo, Liang Chen, Yanyun Qu, Long Sun, Wenhao Wang, Zhenbing Liu, Rushi Lan, Rao Muhammad Umer, Christian Micheloni
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
no code implementations • ICCV 2019 • Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson, Sven Dickinson
Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics.
no code implementations • CVPR 2019 • Morteza Rezanejad, Gabriel Downs, John Wilder, Dirk B. Walther, Allan Jepson, Sven Dickinson, Kaleem Siddiqi
That is, the medial axis based salience weights appear to add useful information that is not available when CNNs are trained to use contours alone.