Tag: Wu

F2-T DeepSLAM: Object Detection, Re-identification and Prediction wih Implicit Mapping

June 30, 2022 in 2021-2022, Project

The project goals and objectives are

1.Communication privacy and security of multi-agent systems:

Develop a privacy-enhanced multi-agent system that uses shared knowledge for both (i) Vision and (ii) Communication tasks.

2.Ego-motion prediction under Intermittent Feedback:

This goal removes the assumption that the GPS signal is always given and considers a GPS denied area. We design a hybrid system to help a traditional error-based control method maintain an error bound, of its state information using a CNN-based localization method.

F12-A Explainable Commonsense Question Answering

June 30, 2022 in 2021-2022, Project

Current question answering systems is incapable of providing human-interpretable explanations or proof to support the decision. In this project, we propose general methods to answer common sense questions, offering natural language explanations or supporting facts. In particular, we propose Copy-explainer that generate natural language explanation that later help answer commonsense questions by leveraging structured and unstructured commonsense knowledge from external knowledge graph and pre-trained language models. Furthermore, we propose Encyclopedia Net, a fact-level causal knowledge graph, facilitating commonsense reasoning for question answering.

F2-T DeepSLAM: Object Detection, Re-identification and Prediction wih Implicit Mapping

June 29, 2022 in 2020-2021, Project

DeepSLAM project aims to Develop an end-to-end deep neural network simultaneously capable of (a) Object Detection, and (b) Re-identification/Tracking
(a) Object Detection using RGB images/videos.
(b) Object Re-identification over multiple frames.
This all-in-one solution provides a better level of information integrity and reuse. In doing so, a local belief of the surrounding area is trained with occupancy grid to generate a local implicit map to capture dynamic road condition. Then local coarse implicit mapping is then combined with global accurate road information for above goals.

F9-V Quasi-Supervised-Learning for SAR Image Recognition

June 29, 2022 in 2020-2021, Project

Current synthetic aperture radar (SAR) image recognition systems suffer from significant degradation when the systems are trained with synthetic images but tested with real measured images. To address this issue, the project is aimed at developing a quasi-supervised-learning approach for SAR image recognition. The key idea is transfer learning with quasi-supervised training procedures.

F10-V Robust PCA with Outlier Mitigation

June 29, 2022 in 2020-2021, Project

Principal Component Analysis (PCA) has been widely used in computer vision and machine learning applications due to its excellent performance in compression, feature extraction and feature representation. However, PCA suffers from severely degraded performance when outliers exist in datasets. To address this issue, the project is intended to develop a robust PCA algorithm, capable of mitigating outliers. The key idea is to leverage a popularity index for each sample so that outliers will contribute little in finding the projection matrix of the PCA.

F2-T DeepSLAM: Object Detection, Re-identification and Prediction wih Implicit Mapping

June 29, 2022 in 2019-2020, Project

DeepSLAM project is designed to develop an end-to-end and all-in-one deep neural network capable of followings, simultaneously, with only road scene RGB images
(a) Object Detection using RGB images and videos;
(b) Object Re-identification over multiple frames, with occlusions;
(c) Predicting multiple object’s future trajectories.
This all-in-one solution provides a better level of information integrity and reuse. In doing so, a local belief of the surrounding area will be trained with grid cells, a navigation system in humans’ brain, to generate a local implicit map to capture dynamic road condition. Then local coarse implicit mapping is then combined with global accurate road information for above three goals.

F3-N OneTask: Online Intelligent Platform for Education and Business

June 29, 2022 in 2019-2020, Project

The goal of this project is to design and develop an intelligent task management for online education and business purpose. The project includes OneTask online platform, self-adaptive neural question generation for study quality evaluation and companion, GCN-based DRL model for self-adaptive neural question generation and study content recommendation and entity-related open-domain question answering system.

F8-M TPR Learning: a Symbolic Neural Approach for Vision Language Intelligence

June 29, 2022 in 2019-2020, Project

Deep learning (DL) has in recent years been widely used in computer vision and natural language processing (NLP) applications due to its superior performance. However, while images and natural languages are known to be rich in structures expressed, for example, by grammar rules, DL has so far not been capable of explicitly representing and enforcing such structures. In this project, we propose an approach to bridging this gap by exploiting tensor product representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate DL with explicit language rules, logical rules, or rules that summarize the human knowledge about the subject.

F3-M TPR Learning: a Symbolic Neural Approach for Vision Language Intelligence

June 29, 2022 in 2018-2019, Project

Deep learning (DL) has in recent years been widely used in computer vision and natural language processing (NLP) applications due to its superior performance. However, while images and natural languages are known to be rich in structures expressed, for example, by grammar rules, DL has so far not been capable of explicitly representing and enforcing such structures [Huang18]. In this project, we propose an approach to bridging this gap by exploiting tensor product representations (TPR) [Smolensky90a, Smolensky90b], a structured neural-symbolic model developed in cognitive science, aiming to integrate DL with explicit language rules, logical rules, or rules that summarize the human knowledge about the subject.