1 code implementation • 25 Jun 2021 • Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao
1) We propose a non-parametric prior distribution over the appearance of image parts so that the latent variable ``what-to-draw'' per step becomes a categorical random variable.
no code implementations • ECCV 2020 • Bo-Wen Chen, Huan Ling, Xiaohui Zeng, Gao Jun, Ziyue Xu, Sanja Fidler
Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy.
1 code implementation • ICCV 2019 • Xiaohui Zeng, Renjie Liao, Li Gu, Yuwen Xiong, Sanja Fidler, Raquel Urtasun
In practice, it performs similarly to the Hungarian algorithm during inference.
1 code implementation • NAACL 2018 • Yu-Siang Wang, Chenxi Liu, Xiaohui Zeng, Alan Yuille
The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49. 67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%.
no code implementations • CVPR 2019 • Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi Keung Tang, Alan L. Yuille
Though image-space adversaries can be interpreted as per-pixel albedo change, we verify that they cannot be well explained along these physically meaningful dimensions, which often have a non-local effect.