- Read more about Hyperspectral Segmentation for Paint Analysis
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- Read more about Reinforced Curriculum Learning for Autonomous Driving in CARLA
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Autonomous Vehicles promise to transport people in a safer, accessible, and even efficient way. Nowadays, real-world autonomous vehicles are build by large teams from big companies with a tremendous amount of engineering effort. Deep Reinforcement Learning can be used instead, without domain experts, to learn end-to-end driving policies. Here, we combine Curriculum Learning with deep reinforcement learning, in order to learn without any prior domain knowledge, an end-to-end competitive driving policy for the CARLA autonomous driving simulator.
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- Read more about Joint Learning On The Hierarchy Representation for Fine-Grained Human Action Recognition
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Fine-grained human action recognition is a core research topic in computer vision. Inspired by the recently proposed hierarchy representation of fine-grained actions in FineGym and SlowFast network for action recognition, we propose a novel multi-task network which exploits the FineGym hierarchy representation to achieve effective joint learning and prediction for fine-grained human action recognition.
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- Read more about Refining the bounding volumes for lossless compression of voxelized point clouds geometry
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- Read more about SOLVING FOURIER PHASE RETRIEVAL WITH A REFERENCE IMAGE AS A SEQUENCE OF LINEAR INVERSE PROBLEMS
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- Read more about CFPNET: CHANNEL-WISE FEATURE PYRAMID FOR REAL-TIME SEMANTIC SEGMENTATION
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Real-time semantic segmentation is playing a more important role in computer vision, due to the growing demand for mobile devices and autonomous driving. Therefore, it is very important to achieve a good trade-off among performance, model size and inference speed. In this paper, we propose a Channel-wise Feature Pyramid (CFP) module to balance those factors. Based on the CFP module, we built CFPNet for real-time semantic segmentation which applied a series of dilated convolution channels to extract effective features.
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- Read more about On Securing Cloud-Hosted Cyber-Physical Systems Using Trusted Execution Environments
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Recently, cloud control systems have gained increasing attention from the research community as a solution to implement networked cyber-physical systems (CPSs). Such an architecture can reduce deployment and maintenance costs albeit at the expense of additional security and privacy concerns. In this paper, first, we discuss state-of-the-art security solutions for cloud control systems and their limitations. Then, we propose a novel control architecture based on Trusted Execution Environments (TEE).
ICAS2021.pdf
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- Read more about Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement Learning
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- Read more about An Ensemble Learning Framework for Multi-class COVID-19 Lesion Segmentation from Chest CT Images
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The novel Coronavirus disease (COVID-19) has been the most critical global challenge over the past months. Lung involvement quantification and distinguishing the types of infections from chest CT scans can assist in accurate severity assessment of COVID-19 pneumonia, efficient use of limited medical resources, and saving more lives. Nevertheless, visual assessment of chest CT images and evaluating the disease severity by radiologists are expensive and prone to error.
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- Read more about Toward Semi autonomous Stiffness Adaptation of Pneumatic Soft Robots: Modeling and Validation
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