1,106 research outputs found
Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval
In this paper we address the problem of learning robust cross-domain
representations for sketch-based image retrieval (SBIR). While most SBIR
approaches focus on extracting low- and mid-level descriptors for direct
feature matching, recent works have shown the benefit of learning coupled
feature representations to describe data from two related sources. However,
cross-domain representation learning methods are typically cast into non-convex
minimization problems that are difficult to optimize, leading to unsatisfactory
performance. Inspired by self-paced learning, a learning methodology designed
to overcome convergence issues related to local optima by exploiting the
samples in a meaningful order (i.e. easy to hard), we introduce the cross-paced
partial curriculum learning (CPPCL) framework. Compared with existing
self-paced learning methods which only consider a single modality and cannot
deal with prior knowledge, CPPCL is specifically designed to assess the
learning pace by jointly handling data from dual sources and modality-specific
prior information provided in the form of partial curricula. Additionally,
thanks to the learned dictionaries, we demonstrate that the proposed CPPCL
embeds robust coupled representations for SBIR. Our approach is extensively
evaluated on four publicly available datasets (i.e. CUFS, Flickr15K, QueenMary
SBIR and TU-Berlin Extension datasets), showing superior performance over
competing SBIR methods
A simplified and novel technique to retrieve color images from hand-drawn sketch by human
With the increasing adoption of human-computer interaction, there is a growing trend of extracting the image through hand-drawn sketches by humans to find out correlated objects from the storage unit. A review of the existing system shows the dominant use of sophisticated and complex mechanisms where the focus is more on accuracy and less on system efficiency. Hence, this proposed system introduces a simplified extraction of the related image using an attribution clustering process and a cost-effective training scheme. The proposed method uses K-means clustering and bag-of-attributes to extract essential information from the sketch. The proposed system also introduces a unique indexing scheme that makes the retrieval process faster and results in retrieving the highest-ranked images. Implemented in MATLAB, the study outcome shows the proposed system offers better accuracy and processing time than the existing feature extraction technique
Image Retrieval Using Image Captioning
The rapid growth in the availability of the Internet and smartphones have resulted in the increase in usage of social media in recent years. This increased usage has thereby resulted in the exponential growth of digital images which are available. Therefore, image retrieval systems play a major role in fetching images relevant to the query provided by the users. These systems should also be able to handle the massive growth of data and take advantage of the emerging technologies, like deep learning and image captioning. This report aims at understanding the purpose of image retrieval and various research held in image retrieval in the past. This report will also analyze various gaps in the past research and it will state the role of image captioning in these systems. Additionally, this report proposes a new methodology using image captioning to retrieve images and presents the results of this method, along with comparing the results with past research
Deep Learning for Free-Hand Sketch: A Survey
Free-hand sketches are highly illustrative, and have been widely used by
humans to depict objects or stories from ancient times to the present. The
recent prevalence of touchscreen devices has made sketch creation a much easier
task than ever and consequently made sketch-oriented applications increasingly
popular. The progress of deep learning has immensely benefited free-hand sketch
research and applications. This paper presents a comprehensive survey of the
deep learning techniques oriented at free-hand sketch data, and the
applications that they enable. The main contents of this survey include: (i) A
discussion of the intrinsic traits and unique challenges of free-hand sketch,
to highlight the essential differences between sketch data and other data
modalities, e.g., natural photos. (ii) A review of the developments of
free-hand sketch research in the deep learning era, by surveying existing
datasets, research topics, and the state-of-the-art methods through a detailed
taxonomy and experimental evaluation. (iii) Promotion of future work via a
discussion of bottlenecks, open problems, and potential research directions for
the community.Comment: This paper is accepted by IEEE TPAM
Deep Image Retrieval: A Survey
In recent years a vast amount of visual content has been generated and shared
from various fields, such as social media platforms, medical images, and
robotics. This abundance of content creation and sharing has introduced new
challenges. In particular, searching databases for similar content, i.e.content
based image retrieval (CBIR), is a long-established research area, and more
efficient and accurate methods are needed for real time retrieval. Artificial
intelligence has made progress in CBIR and has significantly facilitated the
process of intelligent search. In this survey we organize and review recent
CBIR works that are developed based on deep learning algorithms and techniques,
including insights and techniques from recent papers. We identify and present
the commonly-used benchmarks and evaluation methods used in the field. We
collect common challenges and propose promising future directions. More
specifically, we focus on image retrieval with deep learning and organize the
state of the art methods according to the types of deep network structure, deep
features, feature enhancement methods, and network fine-tuning strategies. Our
survey considers a wide variety of recent methods, aiming to promote a global
view of the field of instance-based CBIR.Comment: 20 pages, 11 figure
Deep image retrieval: a survey
In recent years a vast amount of visual content has been generated and shared from various fields, such as social media platforms, medical images, and robotics. This abundance of content creation and sharing has introduced new challenges. In particular, searching databases for similar content, i.e.content based image retrieval (CBIR), is a long-established research area, and more efficient and accurate methods are needed for real time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of intelligent search. In this survey we organize and review recent CBIR works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers. We identify and present the commonly-used benchmarks and evaluation methods used in the field. We collect common challenges and propose promising future directions. More specifically, we focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure, deep features, feature enhancement methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, aiming to promote a global view of the field of instance-based CBIR. Computer Systems, Imagery and Medi
The Theoretical Learning Impact of a Summer Engineering Program Curriculum for Underrepresented Middle School Students
This mixed methods, exploratory and confirmatory study was designed to evaluate the theoretical learning impact of a innovative summer engineering program curriculum would have on its audience, middle school minority students. Several theories were used to develop the innovative curriculum including Human Constructivism, cultural learning styles of African Americans, visual spatial learning and graphic design learning. This study was completed in two phases: evaluation of existing middle school summer engineering program curriculum for best practices and development of innovative curriculum and expert evaluation of the innovative curriculum. Three existing programs from across the country participated in this study. Five engineering education experts evaluated the innovative curriculum. The innovative curriculum is composed of three extensive units that include forces and motion, earth and space science and energy topics. A mixed methods design was used in data collection and analysis to provide a complete view of the theoretical impact of the curriculum. The resulting qualitative and quantitative data indicated the innovative program would enhance its target audience by providing a strong foundation in the fundamental understanding of science and engineering topics and spatial visualization. The qualitative narratives proved that many of the existing programs provide very similar learning environments that do not necessarily include cultural learning, meaningful learning and visual spatial learning. The expert evaluators collectively determined that the innovative program would have a positive and enriching academic impact with the chosen theoretical components. They believed that there was overwhelming evidence (3.7 rating average out of a 4.0) that the theoretical components existed in the curriculum and would provide middle school minority students with the proper knowledge to increase their interest which would inherently increase the science, technology, engineering, and mathematics career pipelines. They also strongly agreed (4.875 rating average out of 5) that the program differed from other program, has relevant learning theories for the target audience exceeded expectations and all the participants of the future program to âsee themselves as engineers.
Design revolutions: IASDR 2019 Conference Proceedings. Volume 4: Learning, Technology, Thinking
In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks â Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices â the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 4 includes papers from Learning, Technology and Thinking tracks of the conference
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Assessing Potential Cognitive Precursors to Math Anxiety: Non-Symbolic Operations and Symbolic Ordinality in Adults
Math anxiety, or a sense of dread related to performing mathematics, affects a wide population of students and adults, but we do not fully understand how math anxiety comes into being. One possibility is the Reduced Capacities Theory, which suggests that natural variations in numeric/spatial capacities are a causal factor in math anxiety. To understand how these numeric capacities relate to math anxiety in adults, this work focuses on three areas that remain underexplored.
Chapter 2 focuses on performing operations on nonsymbolic quantities, which has not yet been tested in relation to math anxiety. We tested the hypothesis that performing addition and subtraction with dots using the Approximate Number System would relate to math anxiety. We asked participants to complete a math anxiety survey, two measures of working memory, a timed symbolic arithmetic test, and a non-symbolic âapproximate arithmeticâ task, in which participants performed addition and subtraction on dot arrays. Using Bayesian analysis and multiple regression, we found evidence for there being no relation between approximate arithmetic performance and math anxiety, suggesting that difficulties performing operations does not constitute a basic number ability linked to math anxiety.
In chapter 3, we measured the relation between number and letter ordinal processing and math anxiety. In separate blocks, we asked participants to determine if triads of numbers and letters were in order (e.g., 4 5 6) or out of order (e.g., C E A) to measure response time and accuracy. Participants also completed a timed arithmetic test to understand the relation between ordinality, arithmetic, and math anxiety. Several hypotheses were assessed including the specificity of math anxiety to numbers (comparing number ordinal trials to letter trials. We found that there was no relation between math anxiety on any measure except that high math anxiety related to slower responses to number ordinal judgement, and that math anxiety mediated the relation between ordinal judgement performance and arithmetic. Together, these data suggest that ordinal processes are unlikely to be a causal factor for math anxiety, despite being critical for early mathematics learning.
In chapter 4, we assessed responses to counting sequences and inhibitory control in relation to math anxiety. We developed a modified Go/No-Go task in which we manipulated trial length, whether they responded to completed vs âviolatedâ (e.g., 21 22 23 vs 21 22 24, respectively) sequences, and distance (violated being +1 or +4, between subjects). Participants also completed a math anxiety survey. We assessed response time, and accuracy to understand counting sequence representationâs relation to MA, and false alarm rates to understand inhibitionâs relation to MA. We found that the high MA group was significantly slower to respond when number to respond to was not consecutive. There were no relations between MA and any other measure.
When viewed together, these data suggest that the Reduced Capacities theory may not be a viable framework for understanding the origin of math anxiety, as all results can be more easily explained by the effects of anxiety on performance. However, because these data were all collected with adults, it remains plausible that children who go on to develop MA may struggle with these capacities during early schooling and see equal gains as their low MA peers. We end by suggesting several potential avenues of research related to MA, focusing on studentsâ and adultsâ emotional interpretation of their math experiences
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