بحث أولي في نظرة المجتمع الإغريقي للمرأة على الصعيد الاجتماعي والاقتصادي، ونستشهد على ذلك من خلال أمثلة من أعمال الشعراء التراجيديين الأوائل (أسخيلوس، سوفوكليس، يوريبيدس)
The task of news article image captioning aims to generate descriptive and informative captions for news article images. Unlike conventional image captions that simply describe the content of the image in general terms, news image captions follow jou
rnalistic guidelines and rely heavily on named entities to describe the image content, often drawing context from the whole article they are associated with. In this work, we propose a new approach to this task, motivated by caption guidelines that journalists follow. Our approach, Journalistic Guidelines Aware News Image Captioning (JoGANIC), leverages the structure of captions to improve the generation quality and guide our representation design. Experimental results, including detailed ablation studies, on two large-scale publicly available datasets show that JoGANIC substantially outperforms state-of-the-art methods both on caption generation and named entity related metrics.
This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly greater than those in the MS COCO Captions dataset. We also show that
CIDEr-D has performance hampered by the lack of multiple reference sentences and high variance of sentence length. To bypass this problem, we introduce CIDEr-R, which improves CIDEr-D, making it more flexible in dealing with datasets with high sentence length variance. We demonstrate that CIDEr-R is more accurate and closer to human judgment than CIDEr-D; CIDEr-R is more robust regarding the number of available references. Our results reveal that using Self-Critical Sequence Training to optimize CIDEr-R generates descriptive captions. In contrast, when CIDEr-D is optimized, the generated captions' length tends to be similar to the reference length. However, the models also repeat several times the same word to increase the sentence length.
In this paper, we present work in progress aimed at the development of a new image dataset with annotated objects. The Multilingual Image Corpus consists of an ontology of visual objects (based on WordNet) and a collection of thematically related ima
ges annotated with segmentation masks and object classes. We identified 277 dominant classes and 1,037 parent and attribute classes, and grouped them into 10 thematic domains such as sport, medicine, education, food, security, etc. For the selected classes a large-scale web image search is being conducted in order to compile a substantial collection of high-quality copyright free images. The focus of the paper is the annotation protocol which we established to facilitate the annotation process: the Ontology of visual objects and the conventions for image selection and for object segmentation. The dataset is designed both for image classification and object detection and for semantic segmentation. In addition, the object annotations will be supplied with multilingual descriptions by using freely available wordnets.
In image captioning, multiple captions are often provided as ground truths, since a valid caption is not always uniquely determined. Conventional methods randomly select a single caption and treat it as correct, but there have been few effective trai
ning methods that utilize multiple given captions. In this paper, we proposed two training technique for making effective use of multiple reference captions: 1) validity-based caption sampling (VBCS), which prioritizes the use of captions that are estimated to be highly valid during training, and 2) weighted caption smoothing (WCS), which applies smoothing only to the relevant words the reference caption to reflect multiple reference captions simultaneously. Experiments show that our proposed methods improve CIDEr by 2.6 points and BLEU4 by 0.9 points from baseline on the MSCOCO dataset.
We study the impact of using rich and diverse textual descriptions of classes for zero-shot learning (ZSL) on ImageNet. We create a new dataset ImageNet-Wiki that matches each ImageNet class to its corresponding Wikipedia article. We show that merely
employing these Wikipedia articles as class descriptions yields much higher ZSL performance than prior works. Even a simple model using this type of auxiliary data outperforms state-of-the-art models that rely on standard features of word embedding encodings of class names. These results highlight the usefulness and importance of textual descriptions for ZSL, as well as the relative importance of auxiliary data type compared to the algorithmic progress. Our experimental results also show that standard zero-shot learning approaches generalize poorly across categories of classes.
The main objective of this research is to study the effect of the accuracy of images'
geometric resolution only on the geometric quality of the resulted three-dimensional
model. In this research, all factors that affect the quality of the model are
fixed and the
geometric resolution is changed only for the used images.
The number of captured images, the number and the distribution and the accuracy of
control points, the camera being used and whether or not it is calibrated, are among the
most important factors influencing the modeling process. In order to neutralize the effect of
the inner parameters of the used camera, a process of calibration was achieved. On the
other hand, we have pre-planned the process of photography to avoid problems resulting
from the lack or increase the number of images, that directly affect the quality and
completeness of the model. In addition, accurate control data obtained from precise survey
work (horizontal geodetic network and leveling network) was applied.
In this study, we examined the effect of image resolution on the generation of a dense
cloud of points by applying the Structure from Motion (SfM) and deducing the surface
model and the orthophoto of a facade of a building at Tishreen University.
The current research aims to know the nature of the relationship
between the psychological needs and the image of self and other on
members of the research sample.
know the differences between high school students and university
students in both of the image of self and other, and the differences
between the two examples mentioned in the psychological needs.
The subject of social responsibilities has recently received the attention of researchers due to its importance; there exists a growing interest in studying the links between CSR and Customers.
The purpose this study is to investigate the impact of
social responsibility dimensions of ALBARAKA BANK on customer’s brand image and customer’s loyalty based on its social responsibility activities, and to identify the concept of social responsibility and its dimensions.
A research framework was developed and data was collected through electronic survey questionnaires from customers of ALBARAKA BANK branches in Syria. In order to obtain the results of the study, the researcher analyzed the 88 questionnaires that were retrieved and then used a number of statistical methods using SPSS program.
The study found that there is an impact of the dimensions of social responsibility (cultural, educational and environmental) on both brand image and customer loyalty. Through these results, the researcher presented a set of recommendations aimed at increasing the bank's activity in terms of social responsibility.
The research aimed to study vegetation change detection of Lattakia province by
using remote sensing techniques, By applicating Normalized Differences Vegetation Index
(NDVI) due to what these techniques had from quickly, accuracy, and completely.
In
addition to saving efforts and money. that change detection methods have showed by
applicating it on Sentinel2 images the plant situation, it's area and distribution in the
studied area.
In addition to knowing plant's cover changes through time passing. Putting geo
databases which benefit in knowing plant situation, and the periodicity supervision for it's
changes.
The yearly and monthly changes of vegetation cover have been studied in Lattakia
province, By making change detection for plants cover of march between (2016-2017).
Then making change detection between march and august of 2017. It was observed that
there were no big changes, Whether increasing or decreasing for plants cover when
studying the yearly changes. While there was big decreasing of plants cover at western
plain areas of province when studying the monthly changes between march and august due
to raising of temperatures. And big increasing of vegetation in high areas.
