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    MCL member Ganning Zhao presented in the SPIE DCS conference

MCL member Ganning Zhao presented in the SPIE DCS conference

Ganning had a trip to Orlando to participate in the SPIE DCS conference. Let’s hear what she would like to share about her work:

Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training. Due to different semantic distributions between synthetic and real-world captured datasets, there exists semantic mismatch between synthetic and refined images, which in turn results in the semantic distortion. Recently, contrastive learning (CL) has been successfully used to pull correlated patches together and push uncorrelated ones apart. In this work, we exploit semantic and structural consistency between synthetic and refined images and adopt CL to reduce the semantic distortion. Besides, we incorporate hard negative mining to improve the performance furthermore. We compare the performance of our method with several other benchmarking methods using qualitative and quantitative measures and show that our method overs the state-of-the-art performance.

— Ganning Zhao

By |May 28th, 2023|News|Comments Off on MCL member Ganning Zhao presented in the SPIE DCS conference|

MCL Genealogical Ancestry Series

Martin Ohm (May 6, 1792 in Erlangen – April 1, 1872 in Berlin) was a German mathematician and a younger brother of physicist Georg Ohm. He earned his doctorate in 1811 at Friedrich-Alexander-University, Erlangen-Nuremberg where his advisor was Karl Christian von Langsdorf. In 1817, he was appointed professor of mathematics and physics in the gymnasium at Thorn. In 1821 he moved to Berlin, and in 1839 became a full professor in the University of Berlin. He delivered courses of lectures at the academy of architecture from 1824 to 1831, and at the schools of artillery and engineering from 1833 to 1852; and he also taught in the military school from 1826 to 1849. Ohm was the first to fully develop the theory of the exponential ab when both a and b are complex numbers in 1823. The 1835 second edition of Ohm’s textbook, Die reine Elementar Mathematik was the first time that Euclid’s ‘extreme and mean ratio’ was given the name of the “golden section” (goldener Schnitt). Ohm’s notable students included Rudolf Lipschitz.

— Xiou Ge


By |May 21st, 2023|News|Comments Off on MCL Genealogical Ancestry Series|
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    Congratulations to MCL Members in Attending PhD Hooding Ceremony

Congratulations to MCL Members in Attending PhD Hooding Ceremony

Ten MCL members attended the Viterbi PhD hooding ceremony on Wednesday, May 10, 2023, in the Bovard Auditorium. They were Pranav Kadam, Xiou Ge, Yao Zhu, Xuejing Lei, Zohreh Azizi, Zhiruo Zhou, Yun Cheng Wang, Yifan Wang, Hongyu Fu, Hong-Shuo Chen. Congratulations to them for their accomplishments in completing their PhD program at USC!

Pranav Kadam received his Master’s degree in Electrical Engineering from USC in 2020 and Bachelor’s degree from Savitribai Phule Pune University, India in 2018. He joined Media Communications Lab in Summer 2019 guided by Prof. C.-C. Jay Kuo. His research interests include 3D Computer Vision and Machine Learning. His thesis is titled “Green Learning for 3D Point Cloud Data Processing”. He will join Tencent America.

Xiou Ge received the B.S. and M.S. degree in electrical and computer engineering from University of Illinois Urbana-Champaign, Urbana, Illinois, in 2016 and 2018 respectively. His research is on knowledge graph embedding and its applications in natural language processing. He is a recipient of USC Annenberg Graduate Fellowship (2019). His thesis is titled “Advanced Knowledge Graph Embedding Techniques: Theory and Applications”. He will join Apple’s AI/ML research team.

Yao Zhu received her Bachelor’s degree in Central South University in Changsha, Hunan, China. She joined Media Communication Lab leading by Professor Kuo in summer 2018. Her research interests include Image Synthesis, 3D construction and Computer Vision.

Xuejing Lei received her Bachelor’s degree in Automation from Xi’an Jiaotong University, China in June 2016, and received her Master’s degree in Electrical Engineering from USC in May 2018. She joined Media Communications Lab in 2017 summer. Her research interests include computer vision and deep learning.

Zohreh Azizi received the B.S. degree in Electrical Engineering from Sharif University of Technology, Iran, in 2018. She joined [...]

By |May 14th, 2023|News|Comments Off on Congratulations to MCL Members in Attending PhD Hooding Ceremony|

Congratulations to Xiou Ge for Passing His Defense

Let us hear what he has to say about his defense and an abstract of his thesis.

Knowledge graphs (KGs) find rich applications in knowledge management and discovery, recommendation systems, fraud detection, chatbots, etc. KGs are directed relational graphs. They are formed by a collection of triples in the form of (head, relation, tail), where heads and tails are called entities and represented by nodes while relations are links in KGs. Since KGs are often incomplete, one critical task in KG is to predict missing information based on available knowledge. Knowledge graph embedding (KGE) is one of the most popular approaches for link prediction and entity typing due to its effectiveness, efficiency, and explainability.

In this thesis, we propose a KGE inspired by compounding three-dimensional geometric transformation named CompoundE3D. Specifically, we apply an adapted beam search algorithm to discover the best performing variants and experiment with different ensemble strategies to achieve state-of-the-art results on four benchmarking datasets. We also analyze the properties of different operators under the CompoundE3D framework and empirically verify their advantages.

I’d like to thank Prof. Kuo for his advising throughout the last 4 years. The working style in MCL has been very effective. Without the weekly reports and meetings, it would probably take me more time to grow. I really respect Prof. Kuo’s research vision and the research direction we embark on will be appreciated in the long term. I’m also very fortunate to be with a group of very nice fellow students in the lab. Everyone is so supportive and willing to help each other. MCL is a perfect fit for students like me who may not be the most talented but are determined to work hard and do good research.

— Xiou [...]

By |May 7th, 2023|News|Comments Off on Congratulations to Xiou Ge for Passing His Defense|

MCL Research on Advanced Object Tracking Technology

Visual tracking is an important task in computer vision and has been integrated into many applications such as autonomous vehicles. Single object tracking (SOT) is the fundamental problem of visual tracking where the tracking is performed on one specific object in the testing video. The ground truth bounding box is provided in the initial frame and the tracker needs to track the object location in all later frames. There could be various challenges such as occlusion, fast motion, viewpoint change, background clutters and so on. The SOT problem has been investigated for a long time, and supervised trackers that are trained offline with large scale labeled data dominate the leader board. The model complexity and computational cost of those large deep neural networks are unaffordable for edge devices. In recent years, unsupervised learning from unlabeled data and lightweight structures start to attract the attention of researchers.

Aiming at lightweight tracking with low computational complexity, we proposed a tracker that learns from the testing sequence per se and does not require any offline training, which further extended the boundary of tracking with low level vision features with comparable performance with recent unsupervised state-of-the-art deep trackers. The number of parameters and the required flops during inference time are much smaller than those of deep trackers. Some sample results are provided in the figures above. Our tracker is able to produce flexible bounding boxes and goes back to the object after occlusion or disappearance. We hope that this work can contribute to the innovations of lightweight tracking and help with better understanding of the roles of feature representations and offline training.

–Zhiruo Zhou

By |April 30th, 2023|News|Comments Off on MCL Research on Advanced Object Tracking Technology|

MCL Research on Green Image Steganalysis

Steganography for spatial images is a technique that involves hiding secret information within digital images in a way that is imperceptible to the human eye. It considers the characteristics and features of the cover media in a local region, and embed the secret information in a manner that is visually or statistically inconspicuous.

As the other side of the coin, steganalysis, is the way of detecting the embedded image (often we call it stego image). There has been numerous works in detecting the hidden information. Traditionally, people use heuristic features and ensemble of machine learning models for detection. It soon becomes feeble in detecting content adaptive steganographic images. After the emerging of neural networks, researchers start to use deep models to detect the weak stego signals by extracting features and doing classification in a whole. Different from the traditional steganalysis and deep learning based steganalysis methods, we propose a novel steganalysis scheme, which is a green steganalysis method.

We first scatter the whole image into small blocks, and then perform anomaly detection on block levels. This step will give us an indication of the likelihood such that this block is embedded or not. Next, we train an embed location detector, to help us locate the blocks that are more discriminant than others. Finally, blocks that are selected from previous step will be fused together and make image-level decision by ensemble classifier. Our architecture is completely explainable, and computationally efficient.

— by Yao Zhu

By |April 22nd, 2023|News|Comments Off on MCL Research on Green Image Steganalysis|
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    MCL Genealogical Ancestry Series – Abraham Gotthelf Kastner

MCL Genealogical Ancestry Series – Abraham Gotthelf Kastner

Abraham Gotthelf Kastner (27 September 1719 – 20 June 1800) was a German mathematician and epigrammatist.

Kästner was the son of law professor Abraham Kästner. Starting from 1731 (aged 12), he studied law, philosophy, physics, mathematics and metaphysics in Leipzig. He was appointed a Notary in 1733, when he was 14. He gained his habilitation (PhD degree in Europe) from the University of Leipzig in 1739 at age 20.  In his early careers, he lectured mathematics, philosophy, logic and law in University of Leipzig after his habilitation. He soon became an associate professor in 1746, at age 27. In 1751 he was elected a member of the Royal Swedish Academy of Sciences, which have famous fellows such as Isaac Newton, Charles Darwin, Alan Turing, Stephen Hawking, etc. In 1756, at his 37, he took up a position as full professor of natural philosophy and geometry at the University of Göttingen. His notable doctoral students include Johann Pfaff, who is the doctoral advisor of Carl Friedrich Gauss). Kästner died in 1800 in Göttingen, at age 81.

Kästner has numerous mathematical writings, including Anfangsgründe der Mathematik (“Foundations of Mathematics”) (Göttingen 1758-69, 4 volumes; 6th edition 1800) and Geschichte der Mathematik (“History of Mathematics”) (Göttingen 1796-1800, 4 volumes). Geschichte der Mathematik is considered an astute work, but lacks a comprehensive overview of all subsections of mathematics. Besides his contribution in math, he is more well-known for his poems, which were notable for their biting humour and sharp irony on different contemporary personalities.

As his descendants, we know Kästner as a talented scholar and devoted researcher in mathematics, philosophy, logic and law. His rich contributions shall be remembered the same as himself.

By |April 16th, 2023|News|Comments Off on MCL Genealogical Ancestry Series – Abraham Gotthelf Kastner|
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    Professor Kuo Delivered Keynote Speech in A Workshop Held by IEEE Hong Kong Section

Professor Kuo Delivered Keynote Speech in A Workshop Held by IEEE Hong Kong Section

Professor C.-C. Jay Kuo, Director of MCL, was invited by the IEEE Hong Kong Chapter to deliver a keynote speech titled “On the 2nd AI wave: toward interpretable, reliable, and sustainable AI” in the 2-Day IEEE Workshop on Deep Learning held at the Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong, on April 3, 2023. In his keynote, Professor Kuo pointed out some concerns with the deep learning methodology commonly employed by AI researchers and presented a new green learning paradigm as an alternative that can achieve comparable performance at a lower computational cost.
During his visit to Hong Kong from April 2-7, Professor Kuo visited several universities and gave the following talks:

“Green learning models for point cloud analysis,” Chinese University of Hong Kong.
“Green learning models for point cloud analysis,” Hong Kong University of Science and Technology.
“A lightweight blind image quality assessment (BIQA) method,” Hong Kong Polytechnic University.
“A lightweight blind image quality assessment (BIQA) method,” City University of Hong Kong.

He used “point cloud analysis” and “blind image quality assessment” as examples, explained how to use the green learning methodology to solve them, and compared the pros and cons of deep learning and green learning.
Furthermore, Professor Kuo gave a special seminar on “How to conduct high-quality research and manage large research groups” to faculty members at the Caritas Institute of Higher Education.

By |April 9th, 2023|News|Comments Off on Professor Kuo Delivered Keynote Speech in A Workshop Held by IEEE Hong Kong Section|

Congratulations to Pranav Kadam for Passing His Defense

Congratulations to Pranav Kadam for passing his defense on Mar. 22. Pranav’s thesis is titled “Green Learning for 3D Point Cloud Data Processing”. His Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Aiichiro Nakano (Outside Member). Here we invite Pranav to share about his PhD thesis and his PhD experience.


Thesis Abstract: 
3D Point Cloud processing and analysis has attracted a lot of attention in present times due to the numerous applications such as in autonomous driving, computer graphics, and robotics. In this dissertation, we focus on the problems of point cloud registration, pose estimation, rotation invariant classification, odometry and scene flow estimation. These tasks are important in the realization of a 3D vision system. Rigid registration aims at finding a 3D transformation consisting of rotation and translation that optimally aligns two point clouds. The next two tasks focus on object-level analysis. For pose estimation, we predict the 6-DOF pose of an object with respect to a chosen frame of reference. Rotation invariant classification aims at classifying 3D objects which are arbitrarily rotated. The latter two problems are for outdoor environments. In odometry, we want to estimate the incremental motion of an object using the point cloud scans captured by it at every instance. While the scene flow estimation task aims at determining the point-wise flow between two consecutive point clouds.
3D perception using point clouds is dominated by deep learning methods nowadays. However, large scale learning on point clouds with deep learning techniques has several issues which are often overlooked. This research is based on the green learning (GL) paradigm and focuses on interpretability, smaller training times and smaller model size. Using GL, we separate the feature learning process from the decision. Features are [...]

By |April 6th, 2023|News|Comments Off on Congratulations to Pranav Kadam for Passing His Defense|

MCL Research on Point-Cloud-based 3D Scene Flow Estimation

3D scene flow aims at finding the point-wise 3D displacement between consecutive point cloud scans. It finds applications in areas such as dynamic scene segmentation and may also guide inter-prediction in compression of dynamically acquired point clouds. We propose a green and interpretable 3D scene flow estimation method for the autonomous driving scenario and name it “PointFlowHop” [1]. We decompose our solution into vehicle ego-motion and object motion components.
The vehicle ego-motion is first compensated using the GreenPCO method which was recently proposed for the task of point cloud odometry estimation. Then, we divide the scene points into two classes – static and moving. The static points do not have any motion and can be assigned only the ego-motion component. The motion of the moving points is analyzed later. For classification, we use a lightweight XGBoost classifier with a 5-dimensional shape and motion feature as the input. Later, moving points are grouped into moving objects using DBSCAN clustering algorithm. Furthermore, the moving objects from the two point clouds are associated using the nearest centroids algorithm. An additional refinement step ensures reclassification of previously misclassified moving points. A rigid flow model is established for each object. Finally, the flow in local regions is refined assuming local scene rigidity.
PointFlowHop method adopts the green learning (GL) paradigm. The task-agnostic nature of the feature learning process in GL enables scene flow estimation through seamless modification and extension of prior related GL methods like R-PointHop and GreenPCO. Furthermore, a large number of operations in PointFlowHop are not performed during training. The ego-motion and object-level motion is optimized in inference only. Similarly, the moving points are grouped into objects only during inference. This makes the training process much faster [...]

By |March 28th, 2023|News, Research|Comments Off on MCL Research on Point-Cloud-based 3D Scene Flow Estimation|