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MCL Research on Natural Image Synthesis

Automatic new image synthesis based on a collection of sample images from the same class finds broad applications in computer graphics and computer vision. Examples include automatic synthesis of human faces, hand-written digits, etc.  On an abstract level, a generative model learns to resemble the probability distribution of data samples and generate new samples based on the learned model.  Research on generative models has attracted rich attention in the machine learning community for decades.

 

Image synthesis is challenging for two main reasons. First, it demands a sufficiently large number of images to define meaningful statistics for a target class.  Second, to generate new images of similar characteristics, one should find one or more effective representations of samples and process them with a proper mechanism. There is a resurge of interests in generative models due to the performance breakthrough achieved by deep learning (DL) technologies in the last 6-7 years. There are however concerns with DL-based generative models. Built upon multi-layer end-to-end optimization, the DL technology is essentially a nonconvex optimization problem. Because of the mathematical complexity associated with nonconvex optimization, DL-based solutions are a black box. Besides, the training of DL-based generative models demands a large amount of computational resource. We propose an explainable and effective generative model to address these concerns, named Successive Subspace Generative (SSG) model.

 

Subspaces of descending dimensions are successively constructed in a feedforward manner, which is called the embedding process. Through embedding, the sample distribution of the source and subsequent subspaces can be captured by embedding parameters and the sample distribution in the core. For generation, samples are first generated according to the learned distribution in the core. Then, they go from the core to the source by traversing the same [...]

By |November 16th, 2020|News|Comments Off on MCL Research on Natural Image Synthesis|
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MCL Research on Video Object Tracking

The visual tracking problem has a long history and has diverse applications in video surveillance, smart traffic system, autonomous driving cars and so on. Deep learning methods have gradually dominated the online single object tracking field because of the superior tracking accuracy. However, they usually require training on tremendous labeled videos which are expensive and time-consuming to acquire.

We proposed an explainable self-supervised salient-point-based approach to track general objects in real time, by utilizing attention and features from both the spatial domain and  the temporal domain. There are two major parts in our tracking system: tracking adjacent frames by matching salient points which represent spatial attention, and utilizing temporal information storing in salient points across different frames to identify loss of object or appearance change. In both parts, the salient point plays an important role in capturing spatial-temporal information. Here the feature of a salient point comes from the concatenation of two hop layer features in two-stage channel-wise Saab. The first hop contains PCA information of local patches at high resolution, while the second hop works at a lower resolution with larger receptive field, thus naturally forming a multi-resolution feature extractor which help capture unusual patterns that we should pay more attention to during tracking.

We have got some preliminary results on the current framework. We evaluate our method on the long-term tracking benchmark TB-50 [1] where the used metrics include success plots and precision plots in one pass evaluation (OPE) mode. This dataset includes 50 video sequences and 29491 frames in total. Mean success rate indicates the average overlapping ratio between the prediction and the ground truth, while mean precision rate shows how close their centers are. The higher the two values are, the better [...]

By |November 8th, 2020|News|Comments Off on MCL Research on Video Object Tracking|
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MCL Research on Spatial Attention

Object detection and recognition is critical to image understanding, and there has been a long competition between supervised and unsupervised approaches in visual attention extraction. We are interested in an unsupervised approach and our method contains two main complimentary parts: Spectral Clustering Segmentation and Contour Detection.

Spectral Clustering has been a mature method for image segmentation, during which images are viewed as graph. For a standard spectral clustering pipeline, usually with each pixel as a vertex, a pixelwise affinity matrix is calculated from the  graph, then the Laplacian matrix of the affinity matrix, and with predefined number of clusters K, Kmeans clustering is conducted with the first K smallest eigenvectors of the Laplacian matrix to give the final segmentation results. In our current method, Pointhop features are adapted instead of the biological features like colors or textures to construct the graph for input image, which is the core contribution to the progress. For each input image, Pointhop features are extracted with channel-wise Saab, and K-neighbors Graph is constructed with the feature map, then combined with the following standard spectral clustering process. To evaluate Segments from Spectral Clustering on Pixelhop, Contour Detection is introduced as complementary middle level features. Here, structure edge [1] detection results are used for contour detection, for each segment from the spectral clustering, the largest closed contours within the segment are evaluated by heuristic rules to check whether a reasonable object or not.

During this process, most objects proposed are parts of a main object, e.g. eyes, face, hand, harms of a human, then during the post process adjacent objects proposed are merged to construct bigger objects, and a full Rectangle Tree of Objects can be constructed for each input image.

 

By Hongyu Fu

By |November 3rd, 2020|News|Comments Off on MCL Research on Spatial Attention|
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MCL Research on Image Super-resolution

Image super-resolution (SR) is a classic image reconstruction problem in computer vision (CV), which aims at recovering a high-resolution image from a low-resolution image. As a type of supervised generative problem, image SR attracts wide attention due to its strong connection with other CV topics, such as object recognition, object alignment, texture synthesis and so on. Besides, it has extensive applications in real world, for example, medical diagnosis, remote sensing, biometric information identification, etc.

For the state-of-the-art approaches for SR, typically there are two mainstreams: 1) example-based learning methods, and 2) Deep Learning (CNN-based) methods. Example-based methods either exploit external low-high resolution exemplar pairs [1], or learn internal similarity of the same image with different resolution scales [2]. However, features used in example-based methods are usually traditional gradient-related or just handcraft, which may affect model performance. While CNN-based SR methods (e.g. SRCNN [3]) does not really distinguish between feature extraction and decision making. Lots of basic CNN models/blocks are applied to SR problem, e.g. GAN, residual learning, attention network, and provide superior SR results. Nevertheless, the non-explainable process and exhaustive training cost are serious drawbacks of CNN-based methods.

By taking advantage of reasonable feature extraction [4], we utilize spatial-spectral compatible cw-Saab features to express exemplar pairs. In addition, we formulate a Successive-Subspace-Learning-based (SSL-based) method to gradually partition data into subspaces by feature statistics, and apply regression in each subspace for better local approximation. By visualization the samples in representative subspaces, we find obvious sample similarity in pixel domain. This demonstrates the efficiency of our method in splitting samples into subspaces with semantic meaning. In the future, we aim at providing such a SSL-based explainable method with high efficiency for SR problem.

—  By Wei Wang

 

Reference:

[1] Timofte, Radu, [...]

By |October 25th, 2020|News|Comments Off on MCL Research on Image Super-resolution|
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MCL Research on Point Cloud Segmentation

Processing and analysis of 3D Point clouds are challenging since the 3D spatial coordinates of points are irregular so that 3D points cannot be properly ordered to be fed into deep neural networks (DNNs). To deal with the order problem, a certain transformation is needed in the deep learning pipeline. Transformation of a point cloud into another form often leads to information loss. Several DNNs have been designed for point cloud classification and segmentation in recent years. They address the point order problem and reach impressive performance in tasks such as classification, segmentation, registration, object detection, etc. However, DNNs rely on expensive labeled data. Furthermore, due to the end-to-end optimization, deep features are learned iteratively via backpropagation. To save both labeling and computational costs, it is desired to obtain features in an unsupervised and feedforward one-pass manner.

Unsupervised or self-supervised feature learning for 3D point clouds was investigated. Although no labels are needed, the learned features are not as powerful as the supervised one with degraded performance. Recently, two light-weight point cloud classification methods, PointHop [1] and PointHop++ [2], were proposed. Both of them have an unsupervised feature learning module, and their performance is comparable with state-of-the-art deep learning methods.

By generalizing the PointHop, we propose a new solution for joint point cloud classification and part segmentation here. Our main contribution is the development of an unsupervised feedforward feature (UFF) learning system [3] with an encoder-decoder architecture. UFF exploits the statistical correlation between points in a point cloud set to learn shape and point features in a one-pass feedforward manner. It obtains the global shape features with an encoder and the local point features using the encoder-decoder cascade. The shape/point features are then fed into classifiers [...]

By |October 18th, 2020|News|Comments Off on MCL Research on Point Cloud Segmentation|
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MCL Research on Point Cloud Registration

Point cloud registration refers to the process of aligning two point clouds. The two point clouds to be aligned are commonly called source and target. The goal is to find a spatial transformation (3D rotation and translation) that needs to be applied to the source to optimally align it with the target.  Registration has become popular with the proliferation of 3D scanning devices like LiDAR and their applications in autonomous driving, robotics, graphics, mapping, etc.

Point clouds need to be registered in order to merge data from different sensors to obtain a globally consistent view, mapping a new observation to known data, etc. Registration is challenging due to several reasons. The source and the target point clouds may have different sampling densities and different number of points. Point clouds may contain outliers and/or be corrupted by noise. Sometimes, only partial views are available.

The problem of registration (or alignment) has been studied for a long while. Prior to point cloud processing, the focus has been on aligning lines, parametric curves and surfaces. The classical Iterative Closest Point (ICP) algorithm alternates between finding corresponding points and estimating the optimal rotation and translation. ICP just uses the spatial coordinates of points to establish point correspondences. More recently there has been a trend to use deep learning, feature based methods for registration. Two such popular methods include PointNetLK and Deep Closest Point (DCP). PointNetLK and DCP treat registration as a supervised learning problem and train end-to-end networks using deep learning. The supervision is in terms of class labels and ground truth rotation matrix and translation vector. We propose a method called ‘Salient Points Analysis (SPA)’ [1] for registration.  In contrast with the recent deep learning methods, our SPA method [...]

By |October 11th, 2020|News|Comments Off on MCL Research on Point Cloud Registration|
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MCL Research on Texture Synthesis

Automatic synthesis of visually pleasant texture that resembles exemplary texture finds applications in computer graphics. We have witnessed amazing quality improvement of synthesized texture in the last 5-6 years due to the resurgence of neural networks. Texture synthesis based on deep learning (DL), such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), yield visually pleasant results. DL-based methods learn transform kernels from numerous training data through end-to-end optimization.  However, these methods have two main shortcomings: 1) lack of mathematical transparency and 2) higher training and inference complexity.

To address these shortcomings, we investigate a non-parametric and interpretable texture synthesis method, called NITES, in this work. NITES is mathematically transparent and efficient in training and inference.  NITES consists of three steps. First, it analyzes the texture patches (as training samples) which are cropped from the input exemplary texture image to obtain its joint spatial-spectral representations. Second, the probabilistic distributions of training samples in the joint spatial-spectral spaces are characterized. The sample distribution in the core subspace was carefully studied, which allows us to build a core subspace generation model. Furthermore, a successive subspace generation model was developed to build a higher-dimensional subspace based on a lower-dimensional subspace. Finally, new texture images are generated by mimicking probabilities and/or conditional probabilities of the source texture patches. In particular, we adopt a data-driven transform, known as the channel-wise (c/w) Saab trans-form, which provides a powerful representation in the joint spatial-spectral space. The c/w Saab transform is derived from the successive subspace learning (SSL) theory.

Experimental results show the superior quality of generated texture images and efficiency of the proposed NITES method in terms of both training and inference time. It can generate visually pleasant texture images effectively, including [...]

By |October 4th, 2020|News, Research|Comments Off on MCL Research on Texture Synthesis|
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Congratulations to Jiali Duan for Passing Qualifying Exam

The title of his Ph.D. thesis proposal is “Theory and Applications of Adversarial and Structured Knowledge Learning”. His qualifying exam committee consisted of C.-C. Jay Kuo (Chair), Keith Michael Chugg, Keith Jenkins, Rahul Jain and Stefanos Nikolaidis.

 

Abstract of thesis proposal:

Deep learning has brought impressive improvements for many tasks, thanks to end-to-end data-driven optimization. However, people have little control over the system during training and limited understanding about the structure of knowledge being learned. In this thesis proposal, we study theory and applications of adversarial and structured knowledge learning: 1) learning adversarial knowledge with human interaction or by incorporating human-in-the-loop; 2) learning structured knowledge by modelling contexts and users’ preferences.

In the first category, our research topics include human-robot adversarial learning; Human-guided curriculum reinforcement learning and PortraitGAN for simultaneous emotion and modality manipulations. In the second category, a real-world compatible recommendation problem was tackled with structural graph representation and deep metric learning. The two categories are also related in the sense that structured knowledge often help lay a solid foundation, on which adversarial knowledge can be learned more successfully. Additionally, we contribute technically by open-sourcing relevant platforms.

By |September 27th, 2020|News|Comments Off on Congratulations to Jiali Duan for Passing Qualifying Exam|
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    Congratulations to Mozhdeh Rouhsedaghat for Her Summer Internship at PayPal

Congratulations to Mozhdeh Rouhsedaghat for Her Summer Internship at PayPal

Mozhdeh Rouhsedaghat received her bachelor’s degree from the EE dept. of Sharif University of Technology. She is currently a Ph.D. student in Media Communications Lab at the University of Southern California, under the supervision of Prof. C.-C. Jay Kuo. Her research interests include computer vision and deep learning. She was a research intern at PayPal during the summer. Here is a short interview with Mozhdeh.

1. How does the study in USC and MCL help you?

During my Ph.D. studies at USC and MCL, I achieved a solid understanding of deep learning and machine learning and strengthened my research skills. So I was able to explore a research area during my internship and achieve great results. At MCL lab, we write weekly reports and hold seminars which helped me improve my writing and presentation skills as well.

2. How was it like working at PayPal?

This year because of the global pandemic, all the interns worked remotely. So PayPal provided the required equipment for all the interns and the University Program Team at PayPal tried to make the whole experience more interesting and exciting. I had daily meetings with my mentor and weekly meetings with my manager. Overall, I was very satisfied with the whole experience.

3. Do you have any suggestions for current graduate students?

When you want to apply for a position make sure that the mentioned responsibilities match your goals. For example, Ph.D. students usually prefer a research position. My second advice is to apply early for the internship positions as most of the positions are offered 5-7 months prior to their start date.

By |September 20th, 2020|News|Comments Off on Congratulations to Mozhdeh Rouhsedaghat for Her Summer Internship at PayPal|
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    Congratulations to Yeji Shen for His Summer Internship at Facebook

Congratulations to Yeji Shen for His Summer Internship at Facebook

Yeji Shen is a PhD candidate in Multimedia Communication Lab (MCL) in USC, supervised by Prof. C.-C. Jay Kuo. He received his Bachelor’s degree in Computer Science from Peking University, Beijing, China in June 2016. Since August 2016, he has been pursuing his PhD degree in MCL. His research interests include Machine Learning, Computer Vision and Artificial Intelligence. During this summer, he did an internship at Facebook. Here is a short interview with Yeji.

1. How does the study in USC and MCL help you?

First of all, in MCL, I learned to have a reasonable understanding of the research topics that I’ve been focusing on, like active learning, 3D vision and some semi-supervised learning. Such understanding is pretty helpful and valuable for both job interviews and the actual working experience. Second, I got to have a reasonable level of presentation skills, which I believe is very important in the future career. Third, a tough mind. Life is challenging. Only those with a tough mind can get through all those difficulties and obtain happiness.

2. How was it like working at Facebook?

The internship this year was a remote one. Different from normal working style, interns needed to work at home with the equipment sent by the company. (Of course, I need to mail them back.) Compared to a normal internship, the main pros are: 1) No need to physically move to the bay area. And thus the fee for house rent was saved. 2) Commuting time was saved. However, it is also clear that some cons are: 1) Harder to communicate. 2) Less interaction with team members. 3) It just didn’t feel good when the remote working style lasts for too long. Still, the overall feeling was not bad.

3. [...]

By |September 13th, 2020|News|Comments Off on Congratulations to Yeji Shen for His Summer Internship at Facebook|
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