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2. A Comprehensive Review of Modern Object Segmentation Approaches

Author

Wang, Yuanbo, Ahsan, Unaiza, Li, Hanyan, and Hagen, Matthew

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4.6
Abstract

Image segmentation is the task of associating pixels in an image with their respective object class labels. It has a wide range of applications in many industries including healthcare, transportation, robotics, fashion, home improvement, and tourism. Many deep learning-based approaches have been developed for image-level object recognition and pixel-level scene understanding-with the latter requiring a much denser annotation of scenes with a large set of objects. Extensions of image segmentation tasks include 3D and video segmentation, where units of voxels, point clouds, and video frames are classified into different objects. We use "Object Segmentation" to refer to the union of these segmentation tasks. In this monograph, we investigate both traditional and modern object segmentation approaches, comparing their strengths, weaknesses, and utilities. We examine in detail the wide range of deep learning-based segmentation techniques developed in recent years, provide a review of the widely used datasets and evaluation metrics, and discuss potential future research directions., Comment: 173 pages, 49 figures, published in Foundations and Trends in Computer Graphics and Vision on 10/4/22. Authors retain copyright

Published
2023
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13. Adversarial recovery of agent rewards from latent spaces of the limit order book

Author

Roa-Vicens, Jacobo, Wang, Yuanbo, Mison, Virgile, Gal, Yarin, and Silva, Ricardo

Subjects
Computer Science - Machine Learning, Quantitative Finance - Trading and Market Microstructure, Statistics - Machine Learning
Abstract

Inverse reinforcement learning has proved its ability to explain state-action trajectories of expert agents by recovering their underlying reward functions in increasingly challenging environments. Recent advances in adversarial learning have allowed extending inverse RL to applications with non-stationary environment dynamics unknown to the agents, arbitrary structures of reward functions and improved handling of the ambiguities inherent to the ill-posed nature of inverse RL. This is particularly relevant in real time applications on stochastic environments involving risk, like volatile financial markets. Moreover, recent work on simulation of complex environments enable learning algorithms to engage with real market data through simulations of its latent space representations, avoiding a costly exploration of the original environment. In this paper, we explore whether adversarial inverse RL algorithms can be adapted and trained within such latent space simulations from real market data, while maintaining their ability to recover agent rewards robust to variations in the underlying dynamics, and transfer them to new regimes of the original environment., Comment: Published as a workshop paper on NeurIPS 2019 Workshop on Robust AI in Financial Services. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada

Published
2019

14. Model-based Reinforcement Learning for Predictions and Control for Limit Order Books

Author

Wei, Haoran, Wang, Yuanbo, Mangu, Lidia, and Decker, Keith

Subjects
Computer Science - Artificial Intelligence
Abstract

We build a profitable electronic trading agent with Reinforcement Learning that places buy and sell orders in the stock market. An environment model is built only with historical observational data, and the RL agent learns the trading policy by interacting with the environment model instead of with the real-market to minimize the risk and potential monetary loss. Trained in unsupervised and self-supervised fashion, our environment model learned a temporal and causal representation of the market in latent space through deep neural networks. We demonstrate that the trading policy trained entirely within the environment model can be transferred back into the real market and maintain its profitability. We believe that this environment model can serve as a robust simulator that predicts market movement as well as trade impact for further studies.

Published
2019

26. Active Object Reconstruction Using a Guided View Planner

Author

Yang, Xin, Wang, Yuanbo, Wang, Yaru, Yin, Baocai, Zhang, Qiang, Wei, Xiaopeng, and Fu, Hongbo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods., Comment: 7 pages,5 figures

Published
2018

38. Palladium-Catalyzed Regioselective [3+2] Cycloadditions of α , β -Unsaturated Imines with Vinylethylene Carbonates: Access to Oxazolidines.

Author

Wang, Yuanbo, Wang, Yue, Sun, Tong, Liu, Qinglin, and Li, Er-Qing

Subjects
*OXAZOLIDINES, *IMINES, *CARBONATES, *RING formation (Chemistry), *PALLADIUM
Abstract

We reported palladium-catalyzed regioselective [3+2] cycloadditions of α,β-unsaturated imines with vinylethylene carbonates, providing the desired oxazolidines in moderate-to-high yields. This reaction provides a facile route for the highly regioselective synthesis of functional oxazolidines. The synthetic utility of the current method was also demonstrated by a gram-scale reaction. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Assessing cell viability with dynamic optical coherence microscopy

Author

Liu, Chao, primary, Smith, Jason, additional, Wang, Yuanbo, additional, Ouellette, Jonathan, additional, Rogers, Jeremy, additional, Oliner, Jonathan, additional, Szulczewski, Michael, additional, Wait, Eric, additional, Brown, William, additional, Wax, Adam, additional, Eliceiri, Kevin, additional, and Rafter, John, additional

Published
2024
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