المؤلفون: Le, Hoang H., Nguyen, Duy M. H., Bhatti, Omair Shahzad, Kopacsi, Laszlo, Ngo, Thinh P., Nguyen, Binh T., Barz, Michael, Sonntag, Daniel

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Understanding human visual processing in dynamic environments is essential for psychology and human-centered interaction design. Mobile eye-tracking systems, combining egocentric video and gaze signals, offer valuable insights. However, manual analysis of these recordings is time-intensive. In this work, we present a novel human-centered learning algorithm designed for automated object recognition within mobile eye-tracking settings. Our approach seamlessly integrates an object detector with an inductive message-passing network technique (I-MPN), harnessing node features such as node profile information and positions. This integration enables our algorithm to learn embedding functions capable of generalizing to new object angle views, thereby facilitating rapid adaptation and efficient reasoning in dynamic contexts as users navigate through their environment. Through experiments conducted on three distinct video sequences, our \textit{interactive-based method} showcases significant performance improvements over fixed training/testing algorithms, even when trained on considerably smaller annotated samples collected through user feedback. Furthermore, we showcase exceptional efficiency in data annotation processes, surpassing approaches that use complete object detectors, combine detectors with convolutional networks, or employ interactive video segmentation.
Comment: First version

الوصول الحر: http://arxiv.org/abs/2406.06239Test

المؤلفون: Ngo, Thinh Phuoc, Dang, Khoa Tran Anh, Luu, Son T., Van Nguyen, Kiet, Nguyen, Ngan Luu-Thuy

مصطلحات موضوعية: Computer Science - Computation and Language

الوصف: This paper presents the development process of a Vietnamese spoken language corpus for machine reading comprehension (MRC) tasks and provides insights into the challenges and opportunities associated with using real-world data for machine reading comprehension tasks. The existing MRC corpora in Vietnamese mainly focus on formal written documents such as Wikipedia articles, online newspapers, or textbooks. In contrast, the VlogQA consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube -- an extensive source of user-uploaded content, covering the topics of food and travel. By capturing the spoken language of native Vietnamese speakers in natural settings, an obscure corner overlooked in Vietnamese research, the corpus provides a valuable resource for future research in reading comprehension tasks for the Vietnamese language. Regarding performance evaluation, our deep-learning models achieved the highest F1 score of 75.34% on the test set, indicating significant progress in machine reading comprehension for Vietnamese spoken language data. In terms of EM, the highest score we accomplished is 53.97%, which reflects the challenge in processing spoken-based content and highlights the need for further improvement.
Comment: To appear as the main conference paper at EACL 2024

الوصول الحر: http://arxiv.org/abs/2402.02655Test

المؤلفون: Le, Hoang H., Nguyen, Cong-Thanh, Ngo, Thinh P., Vinh, Phu V., Nguyen, Binh T., Huynh, Anh T., Nguyen, Hien D.

المصدر: Intelligent Information and Database Systems ; Lecture Notes in Computer Science ; page 97-108 ; ISSN 0302-9743 1611-3349 ; ISBN 9789819958337 9789819958344

الإتاحة: https://doi.org/10.1007/978-981-99-5834-4_8Test