Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO

Grounding DINO and the Segment Anything Model (SAM) have achieved impressive performance in zero-shot object detection and image segmentation, respectively. Together, they have a great potential to revolutionize applications in zero-shot semantic segmentation or data annotation. Yet, in specialized domains like medical image segmentation, objects of interest (e.g., organs, tissues, and tumors) may not fall in existing class names. To address this problem, the referring expression comprehension (REC) ability of Grounding DINO is leveraged to detect arbitrary targets by their language descriptions. However, recent studies have highlighted severe limitation of the REC framework in this application setting owing to its tendency to make false positive predictions when the target is absent in the given image. And, while this bottleneck is central to the prospect of open-set semantic segmentation, it is still largely unknown how much improvement can be achieved by studying the prediction errors. To this end, we perform empirical studies on six publicly available datasets across different domains and reveal that these errors consistently follow a predictable pattern and can, thus, be mitigated by a simple strategy. Specifically, we show that false positive detections with appreciable confidence scores generally occupy large image areas and can usually be filtered by their relative sizes. More importantly, we expect these observations to inspire future research in improving REC-based detection and automated segmentation. Meanwhile, we evaluate the performance of SAM on multiple datasets from various specialized domains and report significant improvements in segmentation performance and annotation time savings over manual approaches

Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods

Data augmentation is arguably the most important regularization technique commonly used to improve generalization performance of machine learning models. It primarily involves the application of appropriate data transformation operations to create new data samples with desired properties. Despite its effectiveness, the process is often challenging because of the time-consuming trial and error procedures for creating and testing different candidate augmentations and their hyperparameters manually. Automated data augmentation methods aim to automate the process. State-of-the-art approaches typically rely on automated machine learning (AutoML) principles. This work presents a comprehensive survey of AutoML-based data augmentation techniques. We discuss various approaches for accomplishing data augmentation with AutoML, including data manipulation, data integration and data synthesis techniques. We present extensive discussion of techniques for realizing each of the major subtasks of the data augmentation process: search space design, hyperparameter optimization and model evaluation. Finally, we carried out an extensive comparison and analysis of the performance of automated data augmentation techniques and state-of-the-art methods based on classical augmentation approaches. The results show that AutoML methods for data augmentation currently outperform state-of-the-art techniques based on conventional approaches

Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning

We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-short learning. Data-driven deep learning models have achieved remarkable performance and demonstrated capabilities surpassing human experts in many applications. Yet, their inability to exploit domain knowledge leads to serious performance limitations in practical applications. In particular, deep learning systems are exposed to adversarial attacks, which can trick them into making glaringly incorrect decisions. Moreover, complex data-driven models typically lack interpretability or explainability, i.e., their decisions cannot be understood by human subjects. Furthermore, models are usually trained on standard datasets with a closed-world assumption. Hence, they struggle to generalize to unseen cases during inference in practical open-world environments, thus, raising the zero- or few-shot generalization problem. Although many conventional solutions exist, explicit domain knowledge, brain-inspired neural network and cognitive architectures offer powerful new dimensions towards alleviating these problems. Prior knowledge is represented in appropriate forms and incorporated in deep learning frameworks to improve performance. Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots. Ultimately, these models achieve better explainability, higher adversarial robustness and data-efficient learning, and can, in turn, provide insights for cognitive science and neuroscience-that is, to deepen human understanding on how the brain works in general, and how it handles these problems

The vulnerability of rice value chains in sub-Saharan Africa: a review

Rice is one of the most important food crops in sub-Saharan Africa. Climate change, variability, and economic globalization threatens to disrupt rice value chains across the subcontinent, undermining their important role in economic development, food security, and poverty reduction. This paper maps existing research on the vulnerability of rice value chains, synthesizes the evidence and the risks posed by climate change and economic globalization, and discusses agriculture and rural development policies and their relevance for the vulnerability of rice value chains in sub-Saharan Africa. Important avenues for future research are identified. These include the impacts of multiple, simultaneous pressures on rice value chains, the effects of climate change and variability on parts of the value chain other than production, and the forms and extent to which different development policies hinder or enhance the resilience of rice value chains in the face of climatic and other pressures

A survey of synthetic data augmentation methods in computer vision

The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of the target task. However, in many scenarios, it is often challenging to obtain sufficient image data for the target task. Data augmentation is a way to mitigate this challenge. A common practice is to explicitly transform existing images in desired ways so as to create the required volume and variability of training data necessary to achieve good generalization performance. In situations where data for the target domain is not accessible, a viable workaround is to synthesize training data from scratch--i.e., synthetic data augmentation. This paper presents an extensive review of synthetic data augmentation techniques. It covers data synthesis approaches based on realistic 3D graphics modeling, neural style transfer (NST), differential neural rendering, and generative artificial intelligence (AI) techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). For each of these classes of methods, we focus on the important data generation and augmentation techniques, general scope of application and specific use-cases, as well as existing limitations and possible workarounds. Additionally, we provide a summary of common synthetic datasets for training computer vision models, highlighting the main features, application domains and supported tasks. Finally, we discuss the effectiveness of synthetic data augmentation methods. Since this is the first paper to explore synthetic data augmentation methods in great detail, we are hoping to equip readers with the necessary background information and in-depth knowledge of existing methods and their attendant issues

Attaining Children’s Development through Appropriate Assessment Practices: Insights from Kindergarten Teachers

The research contributes to the conversation regarding kindergarten teachers’ knowledge about developmentally assessments practices in attaining curriculum goals of selected kindergartens classrooms within the Ghanaian settings. The mixed method research approach was employed. Quantitative data were gathered from 1,413 teachers using questionnaires, while qualitative data were collected from 10 participants who were interviewed and observed. The participants for the quantitative research were randomly selected from ten districts in the Central Region of Ghana. The data were analysed using both descriptive and inferential statistics. However, the participants for the qualitative research were purposely sampled. The qualitative data were analysed through open and axial coding. The study revealed that KG teachers were achieving curriculum goals and were using developmentally appropriate assessment procedures. They were however, not conversant with some of the curriculum goals and emerging assessment practices. It was also evident that there is a positive relationship between KG teachers’ achievement of the goals and the use of assessment procedures. It was recommended among others that through orientation and training sessions, KG teachers should be given insights into KG curriculum goals and diverse authentic assessment procedures to enhance and promote children’s development in varied ways

Student-Teachers' Prior Knowledge as a Predictor of End-of-Semester Exam Performance in Visual Arts Specialism at Bagabaga College of Education

The study aimed at correlating the pre-knowledge of student-teachers to their exam performance in Visual Arts Specialism at Bagabaga College of Education. The main purpose of the study was to use its f indings to inform decisions that would guide subsequent admission criteria in admitting students to study the Visual Arts specialism in the College. Mixed Method approach in the form of survey, narrative and descriptive analyses were employed. Data was collected using online questionnaires and Documentary Analysis Guide. Findings of the study revealed that students’ pre-knowledge of subjects did not match with their College exam grades and that the student-teachers’ self-motivation and mindset were the main determinants of their success in the end of College semester exams. The study, therefore, recommended stakeholders of the College to ensure that students who have the zeal to pursue the Visual Arts Specialism irrespective of their background knowledge should be offered admission into the programme

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The effect of the “Follow in my Green Food Steps” programme on cooking behaviours for improved iron intake : a quasi-experimental randomized community study

Abstract Background Nutritional iron deficiency is one of the leading factors for disease, disability and death. A quasi-experimental randomized community study in South-West Nigeria explored whether a branded behaviour change programme increased the use of green leafy vegetables (greens) and iron-fortified bouillon cubes in stews for improved iron intake. Methods A coinflip assigned the intervention to Ile-Ife (Intervention town). Osogbo (Control town) received no information. At baseline 602 mother-daughter pairs (daughters aged 12–18) were enrolled (Intervention: 300; Control: 302). A Food Frequency Questionnaire assessed the addition of cubes and greens to stews, the primary outcome. Secondary outcomes were the addition of cubes and greens to soups and changes in behavioural determinants measured using the Theory of Planned Behaviour. Structural Equation Modelling (SEM) evaluated the impact of the intervention on behavioural determinants and behaviour. Results The data of 527 pairs was used (Intervention: 240; Control: 287). The increase in greens added to stews was larger in the Intervention town compared to the Control town (MIntervention = 0.3 [SE = 0.03]; MControl = 0.0 [SE = 0.04], p < 0.001, r = 0.36). Change in iron-fortified cubes added to stews did not differ between towns (p = 0.07). The increase in cubes added to soups was larger in the Intervention town compared to the Control Town (MIntervention = 0.9 [SE = 0.2] vs MControl = 0.4 [SE = 0.1], p < .0001, r = 0.20). Unexpectedly, change in greens added to soups was larger in the Control town compared to the Intervention town (MIntervention = − 0.1 [SE = 0.1]; MControl = 0.5 [SE = 0.1], p = 0.003, r = 0.15). The intervention positively influenced awareness of anaemia and the determinants of behaviour in the Intervention town, with hardly any change in the Control town. Baseline SEMs could not be established, so no mediation analyses were done. Post-intervention SEMs highlighted the role of habit in cooking stews. Conclusions The behaviour change programme increased the amount of green leafy vegetables added to stews and iron-fortified cubes added to soups. Future research should assess the long-term impact and the efficacy of the programme as it is scaled up and rolled out