Director of MAPLE research lab
guojunq at gmail dot com
and Learning (MAPLE)
Learning and Pattern Recognition
Vision and Multimedia Computing
Mining and Data Analytics - Knowledge Discovery and Representation
- 2015 Best Paper Runner-up,
International ACM Conference on Multimedia
Best Student Paper Award (co-recipent as the mentor of the student
author), IEEE International Conference on Data Mining (ICDM).
- 2013 "Best of ICDE Paper" by IEEE
Transactions on Knowledge and Data Engineering
IBM Fellowship, IBM
Paper Award, The 15th ACM International Conference on Multimedia (ACM
Best Research Intern, Microsoft Research Asia
Microsoft Fellowship, Microsoft
Scholarship, USTC (Top
Scholarship in USTC)
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- The MAPLE lab is releasing the source codes related with our publications at github [url]. We are sincerely inviting everyone who is interested to try them. Feedbacks and pull requests are strongly welcome.
- We propose a Loss-Sensitive GAN (LS-GAN),
and extend it to a generalized LS-GAN (GLS-GAN) in which
Wasserstein GAN is a special case.
We have proved both distributional consistency and generalizability of the LS-GAN
model in a polynomial
sample complexity in terms of the model size and its Lipschitz
constants. See more
details in our paper [pdf].
- You might be interested in taking a look
at an incomplete map of GANs in our view [url].
- Microsoft CNTK is officially supporting LS-GAN. You can make a side-by-side comparison with the other GAN models at https://www.cntk.ai/pythondocs/CNTK_206C_WGAN_LSGAN.html.
- Our paper "CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces" was accepted by NIPS 2018.
We present a novel capsule projection architecture, setting up a new
state-of-the-art for the capsule nets in literature on CIFAR, SVHN and
ImageNet. The source code was released at our github homepage.
paper "Learning Compact Features for Human Activity Recognition via
Probabilistic First-Take-All" has been accepted by IEEE Transactions on
Pattern Analysis and Machine Intelligence (PAMI). The accepted paper and source code will be released soon.
- Our paper on "Generalized Loss-Sensitive Adversarial Learning with Manifold Margins [pdf]" was accepted by ECCV 2018, where we present to train the Loss-Sensitive GAN by learning a pull-back mapping from a sample x to its projection z onto the manifold
generated by the GAN. We shall its applications into
generating interpolated edits between images as well as
semi-supervised learning with state-of-the-art performances. Source codes are available at [github: torch, blocks].
- Our paper on "A Principled Approach to Hard Triplet Generation via Adversarial Nets " was accepted by ECCV 2018,
where we develop a principled way to generate harder yet more
informative triplets to train query and re-identification models.
State-of-the-art performances were demonstrated on the re-id and fine-grained classification problems.
- Dr. Qi is serving as a senior TPC member for AAAI 2019.
- Our paper on "High sensitivity with tiny candidates for Pulmonary Nodule Detection" was accepted by MICCAI 2018.
- The paper "Global versus Localized Generative Adversarial Nets" will
appear in CVPR 2018. We present a new construction of Laplacian-Beltrami operator
to enable semi-supervised learning on manifolds without resorting to Laplacian graphs
as an approximate. We also demonstrate the
state-of-the-art performance on image classiciation tasks. The source codes are released ad available at [code 1: generation, code 2: semi-supervised learning].
paper "Interleaved Structured Sparse Convolutional Neural Networks"
will appear in CVPR 2018 to present a new compact CNN model.
- Dr. Qi is invited as an area chair for ICPR 2018.
- Dr. Qi is serving as an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (CSVT).
- Dr. Qi will serve as a Technical Program Co-chair for ACM Multimedia 2020 at Seattle.
have a paper "Interleaved Group Group Convolutions for Deep Neural
accepted by ICCV 2017, where a super compact and fast deep
convolutional model was develop that can be deployed on mobile devices. Two
types of group convolutions, a primal group sparse convolution
a dual point-wise permutation convolution, are developed to make the
model more efficient. [pdf]
released our sources for our ICML 2017 and KDD 2017 papers on
State-Frequency LSTM [github, pdf]
and stock price prediction [github, pdf].
- Congratulations to Hao and
Liheng on their ICML2017
papers being accepted.
- Congratulations to Mr. Joey Velez-Ginorio,
an undergraduate researcher of our group, on being selected as
a Barry Goldwater
scholar. This is the most prestigious undergraduate
scholarship across the country established by the United States Congress to support highly qualified college students to pursue
careers in STEM.
- Dr. Qi will serve as an Area Chair for ICCV 2017.
- Dr. Qi is serving as an Area Chair for ICME 2017.
- A paper on learning compact
features that encode dynamics of video and sensor data has been
accepted by ACM TOMM.
- A paper on jointly learning
label classification and tag recommendation has been accepted by AAAI 2017.
- One paper developing an
ranking-based hashing algorithm has been accepted for the publication
in IEEE Transactions on
Pattern Analysis and Machine Intelligence. [pdf]
- One paper
"Tri-Clustered Tensor Completion for Social-Aware Image Tag Refinement"
has been accepted for publication in IEEE Transactions on Pattern
Analysis and Machine Intelligence.
paper has been accepted by
IEEE Transactions on Pattern Analysis and
Machine Intelligence for classifying images of rarely seen
classes with the help of text labels. [pdf][code]
- Two research papers,
including an oral presentation "Hierarchically
Gated Deep Networks for Segmantic Segmentation", have been
accepted for presentation at CVPR 2016, Las Vegas, Nevada. [pdf]
paper has been accepted for plenary presentation at SIGKDD 2016. A fast
detection method for brain disorder based on fMRI was presented. It is
one order of magnitude faster than state-of-the-art methods with even
- Dr. Qi is serving as an Area Chair for ACM Multimedia 2016.
- Dr. Qi will serve as a Senior Program Committee Member
for KDD 2016.
- International Conference on
MultiMedia Modeling will go to Miami FL, 4-6 January 2016 [link].
Dr. Qi will serve as program co-chair.
- CFP: Special Issue on "Big
Media Data: Understanding, Search, and Mining", in IEEE Transactions on
Big Data [pdf]
(deadline: July 1, 2015).
- CFP: "Deep Learning for
Multimedia Computing", in IEEE Transactions on Multimedia [pdf]
(The new deadline is April 20, 2015).
- Our full research paper
"Weekly-Shared Deep Transfer Networks for
Heterogeneous-Domain Knowledge Propagation" has been selected as one
of the four best paper
candidates to be presented at ACM MM 2015.
paper is accepted by ICCV
2015. We developed a novel deep LSTM
model for analyzing human actions, where we explore the differential
structure over memory states to study the dynamic saliency.
full research paper "Weekly-Shared Deep Transfer Networks for
Heterogeneous-Domain Knowledge Propagation" is accepted by ACM MM 2015.
We developed a
novel cross-modal label transfer deep network, showing competitive
performance on predicting image labels derived from the
alignment with text documents.
- Dr. Qi is serving as an Area Chair for ACM Multimedia 2015.
papers are accepted by KDD
2015. Congratulations to Vivek, Rohit, Shiyu and Wei!
In these papers, (1) we developed deep networks to reveal the
brain neural connectivity by aligning time-series
neuron fires that are marked by calcium influx; (2) we
invented a new paradigm
of dynamic model to select and predict sensors and their readings over
time, as compared with the conventional static strategy; and (3) we
heterogeneous networks to predict the cross-modal relevance between
paper "Temporal-Order Preserving Dynamic Quantization for Human Action
Recognition from Multimodal Sensor Streams" accepted by ICMR 2015.
UTKinect-Action dataset, our best approach has achieved 100% accuracy.
to Jun and Kai!
- One paper "Sparse Composite
Quantization" has been accepted by CVPR
2015. Congralutions to Ting!
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