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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

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.

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 [...]

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.