Abstract: ReID is difficult due to person images’ large pose variations and misalignment errors. Existing works use pose estimation, part segmentation, etc. to improve pedestrian representations. Boosting ReID accuracy requires computational overheads and makes deep…
Abstract: Extreme low-light visibility enhancement is difficult. Due to low SNR, image denoising methods may fail in near-darkness. In this paper, we study CMOS photosensor imaging pipeline noise statistics and develop a comprehensive noise model…
Abstract: E-health is growing due to the pandemic and personal healthcare awareness. In many e-health analytics applications, machine learning models improve medical diagnosis. However, in classic cloud-based/centralized e-health paradigms, all data is centrally stored on…
Abstract: Long-term traffic predictions are less dense than short-term ones. City-scale traffic prediction literature is also scarce due to data scarcity. This study examines the scalability of deep learning models for long-term large-scale traffic prediction….
Abstract: Multiple sclerosis (MS) is a chronic inflammatory and degenerative central nervous system disease characterized by focal lesions in the white and gray matter that topographically correlate with a patient’s neurological symptoms and signs. In-vivo…
Abstract: Most urban functional area classification methods today use single-source data analysis and modeling, which cannot fully utilize easy-to-obtain multi-scale and multi-source data. Thus, this paper proposed a multi-modal machine learning-based classification model of urban…
Abstract: Face anti-spoofing (FAS) protects face recognition from presentation attacks. Handcrafted binary or pixel-wise labels supervise PA detectors in FAS methods. Handcrafted labels may not be the best way to supervise PA detectors learning intrinsic…
Abstract: This paper introduces versatile filters to build efficient convolutional neural networks used in many visual recognition tasks. Several methods have been developed to learn compact neural networks for efficient deep learning on cheap hardware….
Abstract: TTE is essential in intelligent transportation systems (ITS). To accurately estimate travel time for multi-city scenarios, fine-grained Trajectory-based Travel Time Estimation (TTTE) is important. However, dynamic temporal and fine-grained spatial dependencies make it difficult….
Abstract: This paper examines how magnification affects content-based image search in digital pathology archives and proposes multi-magnification image representation. Researchers and medical professionals can match patient records in large digital pathology slide archives and learn…
Abstract: This paper introduces a model-reference reinforcement learning algorithm for intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines reinforcement learning with conventional control to improve control accuracy…
Abstract: COVID-19 had infected 127 million people and killed 2.5 million by March 31, 2021. COVID-19 patients and the highly contagious disease must be diagnosed quickly. Since non-contrast chest computed tomography (CT) is clinically useful…
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