A Web video retrieval method using hierarchical structure of Web video groups

In this paper, we propose a Web video retrieval method that uses hierarchical structure of Web video groups. Existing retrieval systems require users to input suitable queries that identify the desired contents in order to accurately retrieve Web videos; however, the proposed method enables retrieval of the desired Web videos even if users cannot input the suitable queries. Specifically, we first select representative Web videos from a target video dataset by using link relationships between Web videos obtained via metadata “related videos” and heterogeneous video features. Furthermore, by using the representative Web videos, we construct a network whose nodes and edges respectively correspond to Web videos and links between these Web videos. Then Web video groups, i.e., Web video sets with similar topics are hierarchically extracted based on strongly connected components, edge betweenness and modularity. By exhibiting the obtained hierarchical structure of Web video groups, users can easily grasp the overview of many Web videos. Consequently, even if users cannot write suitable queries that identify the desired contents, it becomes feasible to accurately retrieve the desired Web videos by selecting Web video groups according to the hierarchical structure. Experimental results on actual Web videos verify the effectiveness of our method

Dialogues with the written world(s): Plurilingual TEAL pedagogy and content learning of Japanese young learners in multilingual landscapes

This ethnographic study aims to describe the literacy development of young Japanese children learning English at an international school in Tokyo (Japan). The research participants, who were recruited from Kindergarten to 4th grade (5 to 10 years old), also participated in summer programs in British Columbia (Canada) for periods ranging from 2 weeks to 2 months. The school adopts a Content and Language Integrated Learning (CLIL) approach (Coyle, Hood & Marsh, 2010), within a Hundred Languages of Children of Reggio Emilia educational approach (Edwards, Gandini & Forman, 1998) and Miyazakian dialogic pedagogy (Miyazaki, 2013). The school also adopts a plurilingual approach to teaching (Lau & Van Viegen, 2020) and used linguistic landscapes as a pedagogical tool (Dagenais, Moore, Sabatier, Lamarre & Armand, 2009) to promote children’s English and content learning through a series of critical inquiries. Methodological tools include classroom ethnography (Heath & Heath, 1983; Frank, Dixon & Green, 1999; Egan-Robertson & Bloome, 1998), Action Research (Wallace, 1998), as well as visual (Pink, 2009) and walking ethnography (Ingold & Vergunst, 2008) to explore the linguistic landscapes with the participants. The analyses are anchored within the theoretical concepts interconnecting plurilingualism (Marshall & Moore, 2018), multiliteracies (Cope & Kalantzis, 2009, New London Group, 1996) and language learning in an asset-oriented perspective on education that views language competence as holistic and plurilingual and intercultural awareness conducive to critical thinking (Coste, Moore & Zarate, 2009). The purpose of the thesis is to build upon the current discussion on plurilingual pedagogies, curriculum design and language instruction for K-12 children, in the context of English teaching and learning in elementary schools in Japan. It has wider implications for teacher education in English as an Additional Language (TEAL) situations

RGMIM: Region-Guided Masked Image Modeling for COVID-19 Detection

Background and objective: Self-supervised learning is rapidly advancing computer-aided diagnosis in the medical field. Masked image modeling (MIM) is one of the self-supervised learning methods that masks a subset of input pixels and attempts to predict the masked pixels. Traditional MIM methods often employ a random masking strategy. In comparison to ordinary images, medical images often have a small region of interest for disease detection. Consequently, we focus on fixing the problem in this work, which is evaluated by automatic COVID-19 identification. Methods: In this study, we propose a novel region-guided masked image modeling method (RGMIM) for COVID-19 detection in this paper. In our method, we devise a new masking strategy that employed lung mask information to identify valid regions to learn more useful information for COVID-19 detection. The proposed method was contrasted with five self-supervised learning techniques (MAE, SKD, Cross, BYOL, and, SimSiam). We present a quantitative evaluation of open COVID-19 CXR datasets as well as masking ratio hyperparameter studies. Results: When using the entire training set, RGMIM outperformed other comparable methods, achieving 0.962 detection accuracy. Specifically, RGMIM significantly improved COVID-19 detection in small data volumes, such as 5% and 10% of the training set (846 and 1,693 images) compared to other methods, and achieved 0.957 detection accuracy even when only 50% of the training set was used. Conclusions: RGMIM can mask more valid lung-related regions, facilitating the learning of discriminative representations and the subsequent high-accuracy COVID-19 detection. RGMIM outperforms other state-of-the-art self-supervised learning methods in experiments, particularly when limited training data is used.Comment: Under revie

Few-shot Personalized Saliency Prediction Based on Inter-personnel Gaze Patterns

This paper presents few-shot personalized saliency prediction based on inter-personnel gaze patterns. In contrast to a general saliency map, a personalized saliecny map (PSM) has been great potential since its map indicates the person-specific visual attention that is useful for obtaining individual visual preferences from heterogeneity of gazed areas. The PSM prediction is needed for acquiring the PSM for the unseen image, but its prediction is still a challenging task due to the complexity of individual gaze patterns. For modeling individual gaze patterns for various images, although the eye-tracking data obtained from each person is necessary to construct PSMs, it is difficult to acquire the massive amounts of such data. Here, one solution for efficient PSM prediction from the limited amount of data can be the effective use of eye-tracking data obtained from other persons. In this paper, to effectively treat the PSMs of other persons, we focus on the effective selection of images to acquire eye-tracking data and the preservation of structural information of PSMs of other persons. In the experimental results, we confirm that the above two focuses are effective for the PSM prediction with the limited amount of eye-tracking data.Comment: 5pages, 3 figure

Soft-Label Anonymous Gastric X-ray Image Distillation

This paper presents a soft-label anonymous gastric X-ray image distillation method based on a gradient descent approach. The sharing of medical data is demanded to construct high-accuracy computer-aided diagnosis (CAD) systems. However, the large size of the medical dataset and privacy protection are remaining problems in medical data sharing, which hindered the research of CAD systems. The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution. Different from model distillation, our method aims to find the optimal distilled images, distilled labels and the optimized learning rate. Experimental results show that the proposed method can not only effectively compress the medical dataset but also anonymize medical images to protect the patient's private information. The proposed approach can improve the efficiency and security of medical data sharing.Comment: Published as a conference paper at ICIP 202

Self-Supervised Learning for Gastritis Detection with Gastric X-Ray Images

We propose a novel self-supervised learning method for medical image analysis. Great progress has been made in medical image analysis because of the development of supervised learning based on deep convolutional neural networks. However, annotating complex medical images usually requires expert knowledge, making it difficult for a wide range of real-world applications ($e.g.$, computer-aided diagnosis systems). Our self-supervised learning method introduces a cross-view loss and a cross-model loss to solve the insufficient available annotations in medical image analysis. Experimental results show that our method can achieve high detection performance for gastritis detection with only a small number of annotations

Dataset Distillation using Parameter Pruning

The acquisition of advanced models relies on large datasets in many fields, which makes storing datasets and training models expensive. As a solution, dataset distillation can synthesize a small dataset that preserves most information of the original large dataset. The recently proposed dataset distillation method by matching network parameters has been proven effective for several datasets. However, the dimension of network parameters is usually large. And we found that a few parameters in the distillation process are difficult to match, which harms the distillation performance. Based on this observation, this paper proposes a new method to solve the problem using parameter pruning. The proposed method can synthesize more robust distilled datasets and improve the distillation performance by pruning difficult-to-match parameters in the distillation process. Experimental results on three datasets show that the proposed method outperformed other state-of-the-art dataset distillation methods

Dataset Distillation for Medical Dataset Sharing

Sharing medical datasets between hospitals is challenging because of the privacy-protection problem and the massive cost of transmitting and storing many high-resolution medical images. However, dataset distillation can synthesize a small dataset such that models trained on it achieve comparable performance with the original large dataset, which shows potential for solving the existing medical sharing problems. Hence, this paper proposes a novel dataset distillation-based method for medical dataset sharing. Experimental results on a COVID-19 chest X-ray image dataset show that our method can achieve high detection performance even using scarce anonymized chest X-ray images