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Congratulations to Fenxiao (Jessica) Chen for Passing His Qualifying Exam on 01/22/19! Her thesis proposal is titled with “GRAPH EMBEDDING WITH DEEP LEARNING TECHNIQUES”. Her Qualifying Exam Committee include: Jay Kuo (Chair), Sandy Sawchuk, Panos Georgiou, Viktor Prasanna and Xiong Ren (Outside Member).

Abstract of thesis proposal:

Graph representation learning is an important task nowadays due to the fact that most real-world data naturally comes in the form of graphs in many applications. Graph data often come in high-dimensional irregular form which makes them more difficult to analyze than the traditional low-dimensional data. Graph embedding has been widely used to convert graph data into a lower dimensional space while preserving the intrinsic properties of the original data.

In this thesis proposal, we specifically study two graph embedding problems: 1) Developing effective and graph embedding techniques can provide researcher with deeper understanding of the collected data more efficiently; 2) Use the embedded information to conduct applications such as node classification and link prediction.

To find an efficient way to learn and encode graph into a low dimensional embedding. We first present a novel Deepwalk-assisted Graph PCA (DGPCA) method is proposed for processing language network data represented by graphs. This method can generate a precise text representation for nodes (or vertices) in language networks. Unlike other existing work, our learned low dimensional vector representations add flexibility in exploring vertices neighborhood information while reducing noise contained in the original data. To demonstrate the effectiveness, we use DGPCA to classify vertices that contain text information in three language networks. Experimentally, DGPCA is shown to perform well on the language datasets in comparison to several state-of-the-art benchmarking methods.

To solve the node prediction problem, we present A novel graph-to-tree conversion mechanism called the deep [...]

Congratulations to Ye Wang for Passing His Qualifying Exam on 01/16/2019! His thesis proposal is titled with “VIDEO OBJECT SEGMENTATION AND TRACKING WITH DEEP LEARNING TECHNIQUES”. His Qualifying Exam committee includes: Jay Kuo (Chair), Sandy Sawchuk, Antonio Ortega, Shri Narayanan, and Joseph Lim.

Abstract of thesis proposal:

Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a given video clip. In this thesis proposal, a novel unsupervised video object segmentation approach via distractor-aware online adaptation (DOA) is proposed. DOA models spatial-temporal consistency in video sequences by capturing background dependencies from adjacent frames. Instance proposals are generated by the instance segmentation network for each frame and then selected by motion information as hard negatives if they exist and positives. To adopt high-quality hard negatives, the block matching algorithm is then applied to preceding frames to track the associated hard negatives. General negatives are also introduced in case that there are no hard negatives in the sequence and experiments demonstrate both kinds of negatives (distractors) are complementary. Finally, we conduct DOA using the positive, negative, and hard negative masks to update the foreground/background segmentation. The proposed approach achieves state-of-the-art results on two benchmark datasets, DAVIS 2016 and FBMS-59 datasets.

In addition, this thesis proposal reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning methods for drone detection is the paucity of training drone images especially thermal drone images. To address this issue, we develop two data augmentation techniques. One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box [...]

Research on graph representation learning has gained increasing attention among researchers because many speech/text data such social networks, linguistic (word co-occurrence) networks, biological networks and many other multi-media domain specific data can be well represented by graphs. Graph representation allows relational knowledge about interacting entities to be stored and accessed efficiently. Analyzing these graph data can provide significant insights into community detection, behavior analysis and many other useful applications for node classification, link prediction and clustering. To analyze the graph data, the first step is to find an accurate and efficient graph representation. The steps of graph embedding are shown in Figure 1. The input is a graph represented by an adjacency matrix. Graph representation learning aims to embed the matrix into a latent dimension that captures the intrinsic characteristics of the original graph. For each node u in the network, we embed it to a d dimensional space that represent the feature of that node, as shown in Figure 2.

Obtaining an accurate representation for the graph is challenging because of several factors. Finding the optimal dimension of the representation is not an easy task. Representation with higher number of dimensions might preserve more information of the original graph at the cost of more space and time. The choose of dimension can also be domain-specific and depends on the type of input graph. Choosing which property of the graph to embed is also challenging given the plethora of properties graphs have.

In our research, we first focus on node prediction task in deep learning models. Specifically, we explore node classification using tree-structured recursive neural networks. Then we switch our goal to improve the accuracy and efficiency of the deep-walk based matrix factorization method.

— By Fenxiao(Jessica) [...]

In 2018, several students graduated from MCL with impressive work and started a new journey of life. Meanwhile, many new blood joined our group and enjoyed a wonderful time exploring in their research areas. In this year, MCL members kept moving forward in research and published high quality papers on top journals and conferences. Year 2018 has been a fruitful year for us.

Now we are standing at the end of 2018. Wish all members have a happy new year and a more wonderful 2019!

Image credits:

Image 1: http://www.traderstrustedacademy.com/category/happy-new-year-2019-hd-images/, cropped and resized with white padding; Image 2: http://www.hdnicewallpapers.com/Wallpaper-Download/New-Year/Happy-New-Year-Red-Rose, cropped and resized with white padding.

Trained deep learning models do not generalize well if the testing data has a different distribution from the training data set. For instance, in medical image segmentation, the MRI and CT scan of the same object look very different. If we simply train a model on the MRI scans, it is very likely that the model will not work on the CT scans. However, it is very expensive and time-consuming to manually label different data sets. Therefore, we wish to transfer the knowledge from a labeled training set to an unlabeled testing data with a different distribution. Domain adaptation can help us achieve this purpose.
Domain adaptation can be categorized into three types based on the availability of target domain data: supervised, semi-supervised, unsupervised [1]. In supervised domain adaptation, a limited amount of labeled target domain data is available. In the semi-supervised setting, unlabeled target domain data as well as a small amount of labeled target domain data is available. In the unsupervised setting, only unlabeled target domain data is available. Unsupervised domain adaptation is an ill-posed problem since we do not have labels for the target domain data. Proper assumptions on the target domain data are important for performing unsupervised domain adaptation. In our research, we focus on the unsupervised domain adaptation. Unsupervised domain adaptation can be applied to many computer vision problems, including classification, segmentation, and detection. Currently, we focus our experiment on classification.
–By Ruiyuan Lin

Reference:
[1] M. Wang and W. Deng, “Deep visual domain adaptation: A survey,” Neurocomputing, 2018.
Image Credits:
Anon, (2018). Available at: http://ai.bu.edu/visda-2018/assets/images/domain-adaptation.png [Accessed 16 Dec. 2018].
X. Peng,  B. Usman,  N. Kaushik,  J. Hoffman, D.  Wang, and K. Saenko, “Visda:  The visual domain adaptation challenge,” 2017.