Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

Research on student engagement in distance learning in sustainability science to design an online intelligent assessment system

Distance learning programs in sustainability science provide a structured curriculum that covers various aspects of sustainability. Despite the growing recognition of distance learning in higher education, existing literature has primarily focused on specific and detailed factors, without a comprehensive summary of the global themes, especially neglecting in-depth exploration of poor engagement factors. This study bridged this gap by not only examining detailed factors but also synthesizing the overarching themes that influenced student engagement. The aim of this study was to investigate the factors that impact student engagement in distance learning within higher education institutions across different countries. By developing a theoretical framework, three key aspects of student engagement in higher education were identified. A total of 42 students and 2 educators affiliated with universities participated in semi-structured interviews. The findings of this paper indicated that sociocultural, infrastructure, and digital equity factors were the main influencing factors of student engagement. Furthermore, a student engagement assessment system was developed using machine learning algorithms to identify students with low levels of engagement and conduct further analysis that considers the three aforementioned factors. The proposed automated approach holds the potential to enhance and revolutionize digital learning methodologies

Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving

A semantic map of the road scene, covering fundamental road elements, is an essential ingredient in autonomous driving systems. It provides important perception foundations for positioning and planning when rendered in the Bird's-Eye-View (BEV). Currently, the prior knowledge of hypothetical depth can guide the learning of translating front perspective views into BEV directly with the help of calibration parameters. However, it suffers from geometric distortions in the representation of distant objects. In addition, another stream of methods without prior knowledge can learn the transformation between front perspective views and BEV implicitly with a global view. Considering that the fusion of different learning methods may bring surprising beneficial effects, we propose a Bi-Mapper framework for top-down road-scene semantic understanding, which incorporates a global view and local prior knowledge. To enhance reliable interaction between them, an asynchronous mutual learning strategy is proposed. At the same time, an Across-Space Loss (ASL) is designed to mitigate the negative impact of geometric distortions. Extensive results on nuScenes and Cam2BEV datasets verify the consistent effectiveness of each module in the proposed Bi-Mapper framework. Compared with exiting road mapping networks, the proposed Bi-Mapper achieves 2.1% higher IoU on the nuScenes dataset. Moreover, we verify the generalization performance of Bi-Mapper in a real-world driving scenario. The source code is publicly available at https://github.com/lynn-yu/Bi-Mapper.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L). The source code is publicly available at https://github.com/lynn-yu/Bi-Mappe

Effect of raw materials on the performance of 3D printing geopolymer: A review

Traditional construction materials such as cement products release a significant amount of carbon dioxide during their preparation and usage, negatively impacting on the environment. In contrast, 3D printing (3DP) with geopolymer materials utilises renewable and low-carbon emission raw materials. It also exhibits characteristics such as energy efficiency and resource-efficient utilisation, contributing to reduction in carbon emissions and an improvement in sustainability. Therefore, the development of 3DP geopolymer holds great significance. This paper provides a comprehensive review of 3DP geopolymer systems, examining the effect of raw materials on processability, including flowability and thixotropy, and microstructure. The study also delves into sustainability and environmental impact. The evaluation highlights the crucial role of silicon, aluminium, and calcium content in the silicate raw material, influencing the gel structure and microstructural development of the geopolymer. Aluminium promotes reaction rate, increases reaction degree, and aids in product formation. Silicon enhances the mechanical properties of geopolymer, while calcium facilitates the formation and stability of the three-dimensional network structure, further improving material strength and stability. Moreover, the reactivity of raw materials is a key factor affecting interlayer bonding and interface mechanical properties. Finally, considering sustainability, the selection of raw materials is crucial in reducing carbon emissions, energy consumption, and costs. Compared to Portland cement, 3DP geopolymer material demonstrate lower carbon emissions, energy consumption, and costs, thus making it a sustainable material

Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation

The recently rising markup-to-image generation poses greater challenges as compared to natural image generation, due to its low tolerance for errors as well as the complex sequence and context correlations between markup and rendered image. This paper proposes a novel model named "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment" (FSA-CDM), which introduces contrastive positive/negative samples into the diffusion model to boost performance for markup-to-image generation. Technically, we design a fine-grained cross-modal alignment module to well explore the sequence similarity between the two modalities for learning robust feature representations. To improve the generalization ability, we propose a contrast-augmented diffusion model to explicitly explore positive and negative samples by maximizing a novel contrastive variational objective, which is mathematically inferred to provide a tighter bound for the model's optimization. Moreover, the context-aware cross attention module is developed to capture the contextual information within markup language during the denoising process, yielding better noise prediction results. Extensive experiments are conducted on four benchmark datasets from different domains, and the experimental results demonstrate the effectiveness of the proposed components in FSA-CDM, significantly exceeding state-of-the-art performance by about 2%-12% DTW improvements. The code will be released at https://github.com/zgj77/FSACDM.Comment: Accepted to ACM MM 2023. The code will be released at https://github.com/zgj77/FSACD

EPCFormer: Expression Prompt Collaboration Transformer for Universal Referring Video Object Segmentation

Audio-guided Video Object Segmentation (A-VOS) and Referring Video Object Segmentation (R-VOS) are two highly-related tasks, which both aim to segment specific objects from video sequences according to user-provided expression prompts. However, due to the challenges in modeling representations for different modalities, contemporary methods struggle to strike a balance between interaction flexibility and high-precision localization and segmentation. In this paper, we address this problem from two perspectives: the alignment representation of audio and text and the deep interaction among audio, text, and visual features. First, we propose a universal architecture, the Expression Prompt Collaboration Transformer, herein EPCFormer. Next, we propose an Expression Alignment (EA) mechanism for audio and text expressions. By introducing contrastive learning for audio and text expressions, the proposed EPCFormer realizes comprehension of the semantic equivalence between audio and text expressions denoting the same objects. Then, to facilitate deep interactions among audio, text, and video features, we introduce an Expression-Visual Attention (EVA) mechanism. The knowledge of video object segmentation in terms of the expression prompts can seamlessly transfer between the two tasks by deeply exploring complementary cues between text and audio. Experiments on well-recognized benchmarks demonstrate that our universal EPCFormer attains state-of-the-art results on both tasks. The source code of EPCFormer will be made publicly available at https://github.com/lab206/EPCFormer.Comment: The source code will be made publicly available at https://github.com/lab206/EPCForme

CANEAT [automated kitty litter box]

Cleaning up pet waste is a daily activity for breeders. This is especially true for cats, as they use litter boxes to urinate and defecate in. It is important to clean the box frequently, otherwise the stink smell will linger in the room. Having someone, or something, who can clean the boxes frequently would make life much easier for breeders. Our prototype, CANEAT, aims to automate the cleaning procedure to make life easier for breeders. The product will filter out the waste and store it into a sealed container.  This container will prevent the smell from leaking into the rest of the room.  Those who are disabled, busy, or travel a lot would also benefit from CANEAT. The device will be assembled using a microcontroller, detecting sensor, power source, and some 3D-printed components. The prototype will be designed and constructed as a stable device which is strong enough to support all the materials. Ultimately, our product will have the ability to scoop the waste and clean the litter box for breeders automatically. Using it will be simple, and we will also develop an IOS/Android app to streamline the procedure further