ZhiruoZhou

Camouflage is an attempt to mask the object into a background image and to match its background. The term “camouflage” originates in the ancient practices of the animal kingdom, where animals would alter their body patterns, textures, and colors to blend in with their environment to evade predators. In military contexts, camouflage is a technique used to conceal soldiers or equipment within the background texture, making it difficult for the enemy to detect them. On the other hand, camouflaged object detection is a method used to uncover the enemy who has used camouflage to hide within the image texture.

Our research objectives are to understand the problem with more insights and develop a pipeline for Camouflaged Object Detection based on the Green Learning framework. The first main technical accomplishments are multiscale color and texture decomposition; since, in this task, the texture is much more important than the color, we decouple the information of textures and colors. The second main technical accomplishment is clustering the images dataset; we cluster similar photos with the matching color histogram together and try to learn the information. It reduces the difficulty of the classifier to separate all kinds of images, and we can focus more on images with similar colors and appearance. We cluster the dataset using HSV color space, quantize the color space into 52 bins, and use K-means clustering to derive the clusters. As shown in Figure, images with similar backgrounds are grouped together. Our experiment shows that training in each subcluster could further reduce training and validation loss.

— Max Chen

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

Source: https://en.wikipedia.org/wiki/Martin_Ohm

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 [...]

Rapid advances in artificial intelligence (AI) in the last decade have been primarily attributed to the applications of deep learning (DL) technologies. DL technologies have been widely applied to speech, audio, image, video, computer graphics, 3D models, and chatbots (e.g., ChatGPTs). These advances are viewed as the first AI wave. There are concerns with the first AI wave. DL solutions are a black box (i.e., not interpretable) and vulnerable to adversarial attacks (i.e., unreliable). Besides, the high carbon footprint yielded by large DL networks is a threat to our environment (i.e., not sustainable).

Many researchers are looking for an alternative solution that is interpretable, reliable, and sustainable. This is expected to be the second AI wave. To this end, MCL members have conducted research on green learning (GL) since 2015. Since GL was inspired by DL, the two share some similarities. As time goes by, the two become more and more different. Low carbon footprints, small model sizes, low computational complexity, and mathematical transparency characterize GL. It offers energy-effective solutions in cloud centers and mobile/edge devices. It has three main modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) decision learning. GL has been successfully applied to a few applications.

The fundamental ideas of GL solution, its demonstrated examples, and its technical outlook were detailed in a recent overview paper by Kuo and Madni, “Green learning: Introduction, examples, and outlook.” Journal of Visual Communication and Image Representation (2022): 103685. We expect to see more GL solutions and applications emerging. All MCL members will be fully devoted to this field in the next decade.

Knowledge graph embedding has been intensively studied in recent years. TransE, RotatE, and PairRE are the most representative and effective among distance-based KG embeddings that make use of simple translation, rotation, and scaling operations respectively to represent relations between entities. These models use simple geometric transformations yet achieve relatively good performance in link prediction. However, it is unclear how to leverage the strength of these individual operations and produce a better embedding model. It turns out that we can treat each operation as a basic building block, from which we can build the most effective KG embedding scoring functions. The composition of geometric operators is a well-established tool for image manipulation. It is natural to apply the same strategy to combine translation, rotation, and scaling operators in the 3D space. Our previous work CompoundE has demonstrated that the composition of affine operations can be an effective strategy in designing KG embedding. We are interested to know whether we can achieve even better results by cascading 3D operators.

We propose to extend our previous work by including more affine operations other than only translation, rotation, and scaling in our framework, and moving from 2D transformations to 3D transformations. In addition, we propose to enhance our CompoundE by addressing two critical issues. First, although Compounding operations lead to plenty of model variants, it is still unclear how to systematically search for a scoring function that performs the best for an individual dataset. We propose to use beam search to gradually build more complex scoring functions from simple but effective variants. To speed up the model tuning during the search, we use the pre-trained entity embedding from base models as initialization. Second, although ensemble learning is a popular [...]