no code implementations • 22 May 2023 • Jiazheng Li, Lin Gui, Yuxiang Zhou, David West, Cesare Aloisi, Yulan He
Traditional methods of automating student answer assessment through text classification often suffer from issues such as lack of trustworthiness, transparency, and the ability to provide a rationale for the automated assessment process.
no code implementations • 13 Apr 2023 • Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar
We demonstrate that appropriate placement of our parameter efficient adapters can significantly reduce the performance gap between frozen pretrained representations and full end-to-end fine-tuning without changes to the original representation and thus preserving original capabilities of the pretrained model.
no code implementations • 6 May 2022 • Alex X. Lee, Coline Devin, Jost Tobias Springenberg, Yuxiang Zhou, Thomas Lampe, Abbas Abdolmaleki, Konstantinos Bousmalis
Our analysis, both in simulation and in the real world, shows that our approach is the best across data budgets, while standard offline RL from teacher rollouts is surprisingly effective when enough data is given.
1 code implementation • CCL 2022 • Mucheng Ren, Heyan Huang, Yuxiang Zhou, Qianwen Cao, Yuan Bu, Yang Gao
Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system -- syndrome differentiation (SD) -- and we introduce the first public large-scale dataset for SD, called TCM-SD.
1 code implementation • 12 Oct 2021 • Alex X. Lee, Coline Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, Jose Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin Riedmiller, Raia Hadsell, Francesco Nori
We study the problem of robotic stacking with objects of complex geometry.
Ranked #2 on Skill Generalization on RGB-Stacking
1 code implementation • EMNLP 2021 • Yuxiang Zhou, Lejian Liao, Yang Gao, Zhanming Jie, Wei Lu
Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA).
1 code implementation • 21 Jan 2021 • Martina Zambelli, Yusuf Aytar, Francesco Visin, Yuxiang Zhou, Raia Hadsell
The sense of touch is fundamental in several manipulation tasks, but rarely used in robot manipulation.
Self-Supervised Learning Robotics
no code implementations • 16 Oct 2020 • Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, Francesco Nori
Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of highly off-policy expert data and on-policy exploration data.
no code implementations • 13 Jan 2020 • Pak Lun Kevin Ding, Zhiqiang Li, Yuxiang Zhou, Baoxin Li
Accelerated MRI scan may be achieved by acquiring less amount of k-space data (down-sampling in the k-space).
no code implementations • 21 Oct 2019 • Rae Jeong, Yusuf Aytar, David Khosid, Yuxiang Zhou, Jackie Kay, Thomas Lampe, Konstantinos Bousmalis, Francesco Nori
In this work, we learn a latent state representation implicitly with deep reinforcement learning in simulation, and then adapt it to the real domain using unlabeled real robot data.
67 code implementations • 2 May 2019 • Jiankang Deng, Jia Guo, Yuxiang Zhou, Jinke Yu, Irene Kotsia, Stefanos Zafeiriou
Face Analysis Project on MXNet
Ranked #2 on Face Detection on WIDER Face (Medium)
no code implementations • CVPR 2019 • Yuxiang Zhou, Jiankang Deng, Irene Kotsia, Stefanos Zafeiriou
3D Morphable Models (3DMMs) are statistical models that represent facial texture and shape variations using a set of linear bases and more particular Principal Component Analysis (PCA).
no code implementations • 25 Mar 2019 • Shiyang Cheng, Michael Bronstein, Yuxiang Zhou, Irene Kotsia, Maja Pantic, Stefanos Zafeiriou
Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data.
no code implementations • CVPR 2017 • Riza Alp Guler, Yuxiang Zhou, George Trigeorgis, Epameinondas Antonakos, Patrick Snape, Stefanos Zafeiriou, Iasonas Kokkinos
We define the regression task in terms of the intrinsic, U-V coordinates of a 3D deformable model that is brought into correspondence with image instances at training time.
no code implementations • CVPR 2018 • Jiankang Deng, Shiyang Cheng, Niannan Xue, Yuxiang Zhou, Stefanos Zafeiriou
We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance.
no code implementations • 23 Aug 2017 • Žiga Emeršič, Dejan Štepec, Vitomir Štruc, Peter Peer, Anjith George, Adil Ahmad, Elshibani Omar, Terrance E. Boult, Reza Safdari, Yuxiang Zhou, Stefanos Zafeiriou, Dogucan Yaman, Fevziye I. Eyiokur, Hazim K. Ekenel
In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions.
no code implementations • 20 Aug 2017 • Jiankang Deng, George Trigeorgis, Yuxiang Zhou, Stefanos Zafeiriou
This encompasses two basic problems: i) the detection and deformable fitting steps are performed independently, while the detector might not provide best-suited initialisation for the fitting step, ii) the face appearance varies hugely across different poses, which makes the deformable face fitting very challenging and thus distinct models have to be used (\eg, one for profile and one for frontal faces).
no code implementations • CVPR 2016 • Yuxiang Zhou, Epameinondas Antonakos, Joan Alabort-i-Medina, Anastasios Roussos, Stefanos Zafeiriou
In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence.