no code implementations • 1 Sep 2023 • Juan lu, Mohammed Bennamoun, Jonathon Stewart, JasonK. Eshraghian, Yanbin Liu, Benjamin Chow, Frank M. Sanfilippo, Girish Dwivedi
Diagnostic investigation has an important role in risk stratification and clinical decision making of patients with suspected and documented Coronary Artery Disease (CAD).
1 code implementation • 26 Jun 2023 • Zhiwei Xu, Hao Wang, Yanbin Liu, Stephen Gould
We explore two differentiable deep declarative layers, namely least squares on sphere (LESS) and implicit eigen decomposition (IED), for learning the principal matrix features (PMaF).
no code implementations • 24 Jun 2023 • Stephen Gould, Ming Xu, Zhiwei Xu, Yanbin Liu
We explore conditions for when the gradient of a deep declarative node can be approximated by ignoring constraint terms and still result in a descent direction for the global loss function.
1 code implementation • CVPR 2023 • Jiahao Zhang, Anoop Cherian, Yanbin Liu, Yizhak Ben-Shabat, Cristian Rodriguez, Stephen Gould
In this paper, we consider a novel setting where such an alignment is between (i) instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video segments from in-the-wild videos; these videos comprising an enactment of the assembly actions in the real world.
no code implementations • 19 Feb 2023 • Haixu Long, Xiaolin Zhang, Yanbin Liu, Zongtai Luo, Jianbo Liu
In this paper, we try to look into the root cause of the LTR task, i. e., training samples for each class are greatly imbalanced, and propose a straightforward solution.
no code implementations • 7 Dec 2022 • Chunyi Sun, Yanbin Liu, Junlin Han, Stephen Gould
Specifically, we use a NeRF model to generate numerous image-angle pairs to train an adjustor, which can adjust the StyleGAN latent code to generate high-fidelity stylized images for any given angle.
1 code implementation • 12 Oct 2022 • Yanbin Liu, Girish Dwivedi, Farid Boussaid, Mohammed Bennamoun
Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability.
no code implementations • 8 Aug 2022 • Yanbin Liu, Girish Dwivedi, Farid Boussaid, Frank Sanfilippo, Makoto Yamada, Mohammed Bennamoun
Novel 3D network architectures are proposed for both the generator and discriminator of the GAN model to significantly reduce the number of parameters while maintaining the quality of image generation.
no code implementations • 29 May 2022 • Shaoshen Wang, Yanbin Liu, Ling Chen, Chengqi Zhang
Empirically, DERM outperformed the state-of-the-art on the unsupervised AD benchmark consisting of 18 datasets.
no code implementations • 1 Jan 2021 • Mathis Petrovich, Chao Liang, Ryoma Sato, Yanbin Liu, Yao-Hung Hubert Tsai, Linchao Zhu, Yi Yang, Ruslan Salakhutdinov, Makoto Yamada
To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence.
1 code implementation • ICCV 2021 • Yanbin Liu, Juho Lee, Linchao Zhu, Ling Chen, Humphrey Shi, Yi Yang
Most existing few-shot classification methods only consider generalization on one dataset (i. e., single-domain), failing to transfer across various seen and unseen domains.
1 code implementation • CVPR 2020 • Yanbin Liu, Linchao Zhu, Makoto Yamada, Yi Yang
Establishing dense correspondences across semantically similar images is a challenging task.
Ranked #6 on Semantic correspondence on PF-WILLOW
1 code implementation • 25 May 2020 • Mathis Petrovich, Chao Liang, Ryoma Sato, Yanbin Liu, Yao-Hung Hubert Tsai, Linchao Zhu, Yi Yang, Ruslan Salakhutdinov, Makoto Yamada
To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence.
1 code implementation • 5 Sep 2019 • Yanbin Liu, Makoto Yamada, Yao-Hung Hubert Tsai, Tam Le, Ruslan Salakhutdinov, Yi Yang
To estimate the mutual information from data, a common practice is preparing a set of paired samples $\{(\mathbf{x}_i,\mathbf{y}_i)\}_{i=1}^n \stackrel{\mathrm{i. i. d.
no code implementations • 11 Apr 2019 • Minseop Park, Jungtaek Kim, Saehoon Kim, Yanbin Liu, Seungjin Choi
A meta-model is trained on a distribution of similar tasks such that it learns an algorithm that can quickly adapt to a novel task with only a handful of labeled examples.
2 code implementations • ICLR 2019 • Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
1 code implementation • 13 Jul 2017 • Linchao Zhu, Yanbin Liu, Yi Yang
In this paper, we present our solution to Google YouTube-8M Video Classification Challenge 2017.
no code implementations • 6 Nov 2015 • Shichao Zhao, Yanbin Liu, Yahong Han, Richang Hong
It achieves the accuracy of 93. 78\% on UCF101 which is the state-of-the-art and the accuracy of 65. 62\% on HMDB51 which is comparable to the state-of-the-art.