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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
About me
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
B-CNN: Branch Convolutional Neural Network for Hierarchical Classification
By Xinqi Zhu, Michael BainPublished in arXiv pre-print, 2017
In this paper, we introduce a variant of the traditional CNN model named the Branch Convolutional Neural Network (B-CNN). A B-CNN model outputs multiple predictions ordered from coarse to fine along the concatenated convolutional layers corresponding to the hierarchical structure of the target classes, which can be regarded as a form of prior knowledge on the output.
Approximated Bilinear Modules for Temporal Modeling
By Xinqi Zhu, Chang Xu, Langwen Hui, Cewu Lu, Dacheng TaoPublished in International Conference on Computer Vision, 2019
We consider two less-emphasized temporal properties of video: 1. Temporal cues are fine-grained; 2. Temporal modeling needs reasoning. To tackle both problems at once, we exploit approximated bilinear modules (ABMs) for temporal modeling.
Learning Disentangled Representations with Latent Variation Predictability
By Xinqi Zhu, Chang Xu, Dacheng TaoPublished in European Conference on Computer Vision, 2020
This paper defines the variation predictability of latent disentangled representations. Given image pairs generated by latent codes varying in a single dimension, this varied dimension could be closely correlated with these image pairs if the representation is well disentangled.
Where and What? Examining Interpretable Disentangled Representations
By Xinqi Zhu, Chang Xu, Dacheng TaoPublished in Conference on Computer Vision and Pattern Recognition (Oral, Best Paper Candidate), 2021
Unlike the independence assumption, interpretability has rarely been exploited to encourage disentanglement in the unsupervised setting. In this paper, we examine the interpretability of disentangled representations by investigating two questions: where to be interpreted and what to be interpreted?
Commutative Lie Group VAE for Disentanglement Learning
By Xinqi Zhu, Chang Xu, Dacheng TaoPublished in International Conference on Machine Learning (Oral/Long Talk), 2021
We view disentanglement learning as discovering an underlying structure that equivariantly reflects the factorized variations shown in data. We propose to encode the data variations with groups, a structure not only can equivariantly represent variations, but can also be adaptively optimized to preserve the properties of data variations.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.