Tam V. Nguyen

• PhD
• Professor (Associate) at University of Dayton

Recent advances in artificial intelligence (AI) have created opportunities to enhance medical decision-making for patients with discordant chronic conditions (DCCs), where a patient has multiple, often unrelated, chronic conditions with conflicting treatment plans. This paper explores the perspectives of healthcare providers (n = 10) and patients (...

Recent few-shot object detection (FSOD) methods have focused on augmenting synthetic samples for novel classes, show promising results to the rise of diffusion models. However, the diversity of such datasets is often limited in representativeness because they lack awareness of typical and hard samples, especially in the context of foreground and ba...

Few-shot object counting aims to count an object of an arbitrary category using only a few annotated exemplars, i.e., few-shot. Existing methods have shown promising results in few-shot counting by using the transformer model combined with convolutions. However, these methods’ approaches to extracting the query and exemplars features using the sepa...

In this paper, we focus on evaluating the robustness of helmet detec tion in the context of traffic surveillance, achieved through state-of-the-art deep learning models. This aims to contribute significantly to motorcycle safety by implementing intelligent systems adept at accurately identifying helmets. An in tegral component of this inquiry ent...

In this paper, we address the recognition of motion illusions in static images. To this end, we collect a new dataset containing images both with and without motion illusions. We then benchmark state-of-the-art deep learning mod els to determine the presence of illusions in the images. Additionally, we assess the role of color in the recognition...

Anomaly detection is an area of video analysis and plays an increasing role in ensuring safety, preventing risks, and guaranteeing quick response in intelligent surveillance systems. It has become a popular research topic and has piqued the interest of researchers in different communities, such as computer vision, machine learning, remote sensing,...

Semantic segmentation plays a crucial role in traffic scene understanding, especially in nighttime conditions. This paper tackles the task of semantic segmentation in nighttime scenes. The largest challenge of this task is the lack of annotated nighttime images to train a deep learning-based scene parser. The existing annotated datasets are abundan...

Image captioning is an exciting yet challenging problem in both computer vision and natural language processing research. In recent years, this problem has been addressed by Transformer-based models optimized with Cross-Entropy loss and boosted performance via Self-Critical Sequence Training. Two types of representations are embedded into captionin...

Sketch-to-image synthesis method transforms a simple abstract black-and-white sketch into an image. Most sketch-to-image synthesis methods generate an image in an end-to-end manner, leading to generate a non-satisfactory result. The reason is that, in end-to-end models, the models generate images directly from the input sketches. Thus, with very ab...

Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. However, this mechanism d...

Sketch classification plays a crucial role across diverse domains, including image retrieval, artistic style analysis, and content-based image retrieval. While CNNs have demonstrated remarkable success in various image-related tasks, the computational complexity of large models poses challenges in resource-constrained environments. To address this...

In this paper, we address the crucial task of brain tumor segmentation in medical imaging and propose innovative approaches to enhance its performance. The current state-of-the-art nnU-Net has shown promising results but suffers from extensive training requirements and underutilization of pre-trained weights. To overcome these limitations, we integ...

Information retrieval is vital in our daily lives, with applications ranging from job searches to academic research. In today’s data-driven world, efficient and accurate retrieval systems are crucial. Our research focuses on video data, using a system called LUMOS-DM: Landscape-based Multimodal Scene Retrieval Enhanced by Diffusion Model. This syst...

This paper focuses on addressing the complex healthcare needs of patients struggling with discordant chronic comorbidities (DCCs). Managing these patients within the current healthcare system often proves to be a challenging process, characterized by evolving treatment needs necessitating multiple medical appointments and coordination among differe...

Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data on concealed objects such as camouflaged animals in natural scenes. In this paper, we address the problem of few-shot learning for camouflaged object detection and segmentation. To this end, we first col...

The real estate industry relies heavily on accurately predicting the price of a house based on numerous factors such as size, location, amenities, and season. In this study, we explore the use of machine learning techniques for predicting house prices by considering both visual cues and estate attributes. We collected a dataset (REPD-3000) of 3000...

Context-aware safety monitoring based on computer vision has received relatively little attention, although it is critical for recognizing the working context of workers and performing precise safety assessment with respect to Personal Protective Equipment (PPE) compliance checks. To address this knowledge gap, this study proposes vision-based moni...

The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve...

Sketch-to-image is an important task to reduce the burden of creating a color image from scratch. Unlike previous sketch-to-image models, where the image is synthesized in an end-to-end manner, leading to an unnaturalistic image, we propose a method by decomposing the problem into subproblems to generate a more naturalistic and reasonable image. It...

Camouflaged object detection (COD) and camouflaged instance segmentation (CIS) aim to recognize and segment objects that are blended into their surroundings, respectively. While several deep neural network models have been proposed to tackle those tasks, augmentation methods for COD and CIS have not been thoroughly explored. Augmentation strategies...

Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection methods focus on extracting the features of a few samples of novel classes that lack diversity. Hence, they may not be sufficient to capture the data distribution. To address...

The fashion e-commerce industry has witnessed significant growth in recent years, prompting exploring image-based virtual try-on techniques to incorporate Augmented Reality (AR) experiences into online shopping platforms. However, existing research has primarily overlooked a crucial aspect - the runtime of the underlying machine-learning model. Whi...

Interior design is crucial in creating aesthetically pleasing and functional indoor spaces. However, developing and editing interior design concepts requires significant time and expertise. We propose Virtual Interior DESign (VIDES) system in response to this challenge. Leveraging cutting-edge technology in generative AI, our system can assist user...

Background and objective: Pulmonary nodule detection and segmentation are currently two primary tasks in analyzing chest computed tomography (Chest CT) in order to detect signs of lung cancer, thereby providing early treatment measures to reduce mortality. Even though there are many proposed methods to reduce false positives for obtaining effectiv...

Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship between categories. In this paper, we exploit the hierarchical information to leverage discriminative and relevant features of base...

Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship between categories. In this paper, we exploit the hierarchical information to leverage discriminative and relevant features of base...

Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data of camouflaged objects such as camouflaged animals in natural scenes. In this paper, we address the problem of few-shot learning for camouflaged object detection and segmentation. To this end, we first c...

The retrieval of 3D objects has gained significant importance in recent years due to its broad range of applications in computer vision, computer graphics, virtual reality, and augmented reality. However, the retrieval of 3D objects presents significant challenges due to the intricate nature of 3D models, which can vary in shape, size, and texture,...

Free access util 06/03/2023: https://authors.elsevier.com/c/1gvwW_L4MYIlrQ ======================================================= Image segmentation-based applications have been actively investigated. However, it is non-trivial to prepare polygon annotations. Previous studies suggested pseudo label generation methods based on weakly supervised le...

Portrait cartoonization aims at translating a portrait image to its cartoon version, which guarantees two conditions, namely, reducing textural details and synthesizing cartoon facial features (e.g., big eyes or line-drawing nose). To address this problem, we propose a two-stage training scheme based on GAN, which is powerful for stylization proble...

Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. However, this mechanism i...

Image synthesis is a process of converting the input text, sketch, or other sources, i.e., another image or mask, into an image. It is an important problem in the computer vision field, where it has attracted the research community to attempt to solve this challenge at a high level to generate photorealistic images. Different techniques and strateg...

Free access until 04/06/2023 at https://authors.elsevier.com/c/1gbOt3IhXMxU96

Anomaly detection plays an increasingly important role in video surveillance and is one of the issues that has attracted various communities, such as computer vision, machine learning, and data mining in recent years. Moreover, drones equipped with cameras have quickly been deployed to a wide range of applications, starting from border security app...

The COVID-19 pandemic presents significant challenges due to its high transmissibility and mortality risk. Traditional diagnostic methods, such as RT-PCR, have limitations that hinder timely and accurate screening. In response, AI-powered computer-aided imaging analysis techniques have emerged as a promising alternative for COVID-19 diagnosis. In t...

Photobombing occurs very often in photography. This causes inconvenience to the main person(s) in the photos. Therefore, there is a legitimate need to remove the photobombing from taken images to produce a pleasing image. In this paper, the aim is to conduct a benchmark on this aforementioned problem. To this end, we first collect a dataset of imag...

The price of a house depends on many factors, such as its size, location, amenities, surrounding establishments, and the season in which the house is being sold, just to name a few of them. As a seller, it is absolutely essential to price the property competitively else it will not attract any buyers. This problem has given rise to multiple compani...

Natural user interaction in virtual environment is a prominent factor in any mixed reality applications. In this paper, we revisit the assessment of natural user interaction via a case study of a virtual aquarium. Viewers with the wearable headsets are able to interact with virtual objects via head orientation, gaze, gesture, and visual markers. Th...

Anomaly analysis is an important component of any surveillance system. In recent years, it has drawn the attention of the computer vision and machine learning communities. In this article, our overarching goal is thus to provide a coherent and systematic review of state-of-the-art techniques and a comprehensive review of the research works in anoma...

Counting multi-vehicle motions via traffic cameras in urban areas is crucial for smart cities. Even though several frameworks have been proposed in this task, there is no prior work focusing on the highly common, dense and size-variant vehicles such as motorcycles. In this paper, we propose a novel framework for vehicle motion counting with adaptiv...

In this paper, we propose contextual guided segmentation (CGS) framework for video instance segmentation in three passes. In the first pass, i.e.,preview segmentation, we propose Instance Re-Identification Flow to estimate main properties of each instance (i.e., human/non-human, rigid/deformable, known/unknown category) by propagating its preview m...

Although previous research laid the foundation for vision‐based monitoring systems using convolutional neural networks (CNNs), too little attention has been paid to the challenges associated with data imbalance and varying object sizes in far‐field monitoring. To fill the knowledge gap, this paper investigates various loss functions to design a cus...

Few-shot learning is proposed to overcome the problem of scarce training data in novel classes. Recently, few-shot learning has been well adopted in various computer vision tasks such as object recognition and object detection. However, the state-of-the-art (SOTA) methods have less attention to effectively reuse the information from previous stages...

In this article, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed cross-modal info-max hashing (CMIMH). First, to learn informative representations that can preserve both intramodal and intermodal simil...

In recent decades, digital transformation has received growing attention worldwide, that has leveraged the explosion of digitized document data. In this paper, we address the problem of parsing publications, in particular, Vietnamese publications. The Vietnamese publications are well-known with high variant, diverse layouts, and some characters are...

Document image understanding is increasingly useful since the number of digital documents is increasing day-by-day and the need for automation is increasing. Object detection plays a significant role in detecting vital objects and layouts in document images and contributes to providing a clearer understanding of the documents. Nonetheless, previous...

In this paper, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed Cross-Modal Info-Max Hashing (CMIMH). First, to learn informative representations that can preserve both intra- and inter-modal similariti...

Recent years have witnessed great advances in object segmentation research. In addition to generic objects, aquatic animals have attracted research attention. Deep learning-based methods are widely used for aquatic animal segmentation and have achieved promising performance. However, there is a lack of challenging datasets for benchmarking. Therefo...

This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged object segmentation) in terms of qu...

In recent years, the need to exploit digitized document data has been increasing. In this paper, we address the problem of parsing digitized Vietnamese paper documents. The digitized Vietnamese documents are mainly in the form of scanned images with diverse layouts and special characters introducing many challenges. To this end, we first collect th...

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and...

In recent years, the need to exploit digitized document data has been increasing. In this paper, we address the problem of parsing digitized Vietnamese paper documents. The digitized Vietnamese documents are mainly in the form of scanned images with diverse layouts and special characters introducing many challenges. To this end, we first collect th...

Polyps detection plays an important role in colonoscopy, cancer diagnosis, and early treatment. Many efforts have been made to improve the encoder-decoder framework using the global feature with an attention mechanism to enhance local features, helping to effectively segment diversity polyps. However, using only global information derived from the...

In this paper, we propose Contextual Guided Segmentation (CGS) framework for video instance segmentation in three passes. In the first pass, i.e., preview segmentation, we propose Instance Re-Identification Flow to estimate main properties of each instance (i.e., human/non-human, rigid/deformable, known/unknown category) by propagating its preview...

Camouflaged objects attempt to conceal their texture into the background and discriminating them from the background is hard even for human beings. The main objective of this paper is to explore the camouflaged object segmentation problem, namely, segmenting the camouflaged object(s) for a given image. This problem has not been well studied in spit...

In this paper, we investigate the interesting yet challenging problem of camouflaged instance segmentation. To this end, we first annotate the available CAMO dataset at the instance level. We also embed the data augmentation in order to increase the number of training samples. Then, we train different state-of-the-art instance segmentation on the C...

In this paper, we introduce a practical system for interactive video object mask annotation, which can support multiple back-end methods. To demonstrate the generalization of our system, we introduce a novel approach for video object annotation. Our proposed system takes scribbles at a chosen key-frame from the end-users via a user-friendly interfa...

This paper pushes the envelope on camouflaged regions to decompose them into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation in-the-wild, we introduce a new dataset, namely CAMO++, by extending our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and d...

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerabl...

This paper proposes two novel techniques to train deep convolutional neural networks with low bit-width weights and activations. First, to obtain low bit-width weights, most existing methods obtain the quantized weights by performing quantization on the full-precision network weights. However, this approach would result in some mismatch: the gradie...

In this paper, we investigate the interesting yet challenging problem of camouflaged instance segmentation. To this end, we first annotate the available CAMO dataset at the instance level. We also embed the data augmentation in order to increase the number of training samples. Then, we train different state-of-the-art instance segmentation on the C...

In this paper, we introduce a practical system for interactive video object mask annotation, which can support multiple back-end methods. To demonstrate the generalization of our system, we introduce a novel approach for video object annotation. Our proposed system takes scribbles at a chosen key-frame from the end-users via a user-friendly interfa...

Modern malware evolves various detection avoidance techniques to bypass the state‐of‐the‐art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning models to classify and detect malware. However, existing works in this field only perform simple image transformation methods. These...

In this work, we introduce a practical system which synthesizes an appealing image from natural language descriptions such that the generated image should maintain the aesthetic level of photographs. Our proposed method takes the text from the end-users via a user friendly interface and produces a set of different label maps via the primary generat...

Recent growth in deep learning and computer vision has opened up many opportunities for advanced intelligent systems. While the dataset quality plays a crucial role in the training phase and also affects the performance of a model, creating a reliable and diverse dataset appears to be challenging. Therefore, data augmentation can be used as a prepr...

This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval. The framework is a two-step hashing approach which decouples the optimization into (1) binary optimization and (2) hashing function learning. In the first st...

This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval. The framework is a two-step hashing approach which decouples the optimization into (1) binary optimization and (2) hashing function learning. In the first st...

Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and mirror stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our prop...

This paper proposes two novel techniques to train deep convolutional neural networks with low bit-width weights and activations. First, to obtain low bit-width weights, most existing methods obtain the quantized weights by performing quantization on the full-precision network weights. However, this approach would result in some mismatch: the gradie...

In this paper, we propose a novel Multi-Referenced Guided Instance Segmentation (MR-GIS) framework for the challenging problem of semi-supervised video instance seg-mentation. Our proposed method consists two passes of segmentation with mask guidance. First, we quickly propagate an initial mask to all frames in a sequence to create an initial segme...

CAPTCHA, a short form of “Completely Automated Public Turing test to Tell Computers and Humans Apart”, is a computer program that can generate and grade tests that most humans can pass, but current computer programs cannot. This paper advances research on image-based CAPTCHA by incorporating the object segment collages. We first collect object segm...

Unmanned aircraft systems or drones enable us to record or capture many scenes from the bird’s-eye view and they have been fast deployed to a wide range of practical domains, i.e., agriculture, aerial photography, fast delivery and surveillance. Object detection task is one of the core steps in understanding videos collected from the drones. Howeve...