537 research outputs found
Pre-trained convolutional networks for classification of training leather image
Leather craft products, such as belt, gloves, shoes,
bag, and wallet are mainly originated from cow, crocodile, lizard,
goat, sheep, buffalo, and stingray skin. Before the skins are used
as leather craft materials, they go through a tanning process.
With the rapid development of leather craft industry, an
automation system for leather tanning factories is important to
achieve large scale production in order to meet the demand of
leather craft materials. The challenges in automatic leather
grading system based on type and quality of leather are the skin
color and texture after tanning process will have a large variety
within the same skin category and have high similarity with the
other skin categories. Furthermore, skin from different part of
animal body may have different color and texture. Therefore, a
leather classification method on tanning leather image is proposed. The method uses pre-trained deep convolution neural network (CNN) to extract rich features from tanning leather image and Support Vector Machine (SVM) to classify the features into several types of leather. Performance evaluation shows that the proposed method can classify various types of
leather with good accuracy and superior to other state-of-the-art leather classification method in terms of accuracy and computational time
Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation
Visual Word Sense Disambiguation (VWSD) is a novel challenging task with the
goal of retrieving an image among a set of candidates, which better represents
the meaning of an ambiguous word within a given context. In this paper, we make
a substantial step towards unveiling this interesting task by applying a
varying set of approaches. Since VWSD is primarily a text-image retrieval task,
we explore the latest transformer-based methods for multimodal retrieval.
Additionally, we utilize Large Language Models (LLMs) as knowledge bases to
enhance the given phrases and resolve ambiguity related to the target word. We
also study VWSD as a unimodal problem by converting to text-to-text and
image-to-image retrieval, as well as question-answering (QA), to fully explore
the capabilities of relevant models. To tap into the implicit knowledge of
LLMs, we experiment with Chain-of-Thought (CoT) prompting to guide explainable
answer generation. On top of all, we train a learn to rank (LTR) model in order
to combine our different modules, achieving competitive ranking results.
Extensive experiments on VWSD demonstrate valuable insights to effectively
drive future directions.Comment: Conference on Empirical Methods in Natural Language Processing
(EMNLP) 202
Analysing Scientific Collaborations of New Zealand Institutions using Scopus Bibliometric Data
Scientific collaborations are among the main enablers of development in small
national science systems. Although analysing scientific collaborations is a
well-established subject in scientometrics, evaluations of scientific
collaborations within a country remain speculative with studies based on a
limited number of fields or using data too inadequate to be representative of
collaborations at a national level. This study represents a unique view on the
collaborative aspect of scientific activities in New Zealand. We perform a
quantitative study based on all Scopus publications in all subjects for more
than 1500 New Zealand institutions over a period of 6 years to generate an
extensive mapping of scientific collaboration at a national level. The
comparative results reveal the level of collaboration between New Zealand
institutions and business enterprises, government institutions, higher
education providers, and private not for profit organisations in 2010-2015.
Constructing a collaboration network of institutions, we observe a power-law
distribution indicating that a small number of New Zealand institutions account
for a large proportion of national collaborations. Network centrality concepts
are deployed to identify the most central institutions of the country in terms
of collaboration. We also provide comparative results on 15 universities and
Crown research institutes based on 27 subject classifications.Comment: 10 pages, 15 figures, accepted author copy with link to research
data, Analysing Scientific Collaborations of New Zealand Institutions using
Scopus Bibliometric Data. In Proceedings of ACSW 2018: Australasian Computer
Science Week 2018, January 29-February 2, 2018, Brisbane, QLD, Australi
Advances in Postharvest Process Systems
This Special Issue presents a range of recent technologies and innovations to help the agricultural and food industry to manage and minimize postharvest losses, enhance reliability and sustainability, and generate high-quality products that are both healthy and appealing to consumers. It focuses on three main topics of food storage and preservation technologies, food processing technologies, and the applications of advanced mathematical modelling and computer simulations. This presentation of the latest research and information is particularly useful for people who are working in or associated with the fields of agriculture, the agri-food chain and technology development and promotion
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