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image captioning

Mingxing Zhang, Yang Yang, Hanwang Zhang, Yanli Ji, Heng Tao Shen, Tat-Seng Chua
Recently, a great progress in automatic image captioning has been achieved by using semantic concepts detected from the image. However, we argue that existing concepts-tocaption framework, in which the concept detector is trained using the image-caption pairs to minimize the vocabulary discrepancy, suffers from the deficiency of insufficient concepts. The reasons are two-fold: 1) the extreme imbalance between the number of occurrence positive and negative samples of the concept; and 2) the incomplete labelling in training captions caused by the biased annotation and usage of synonyms...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Senmao Ye, Nian Liu, Junwei Han
We propose a novel attention framework called attentive linear transformation (ALT). Instead of learning the spatial or channel-wise attention in existing models, ALT learns to attend to the high-dimensional transformation matrix from the image feature space to the context vector space. Thus ALT can learn various relevant feature abstractions, including spatial attention, channel-wise attention and visual dependence. Besides, we propose a soft threshold regression to predict the attention probabilities for local regions...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Cesc Chunseong Park, Byeongchang Kim, Gunhee Kim
We address personalized image captioning, which generates a descriptive sentence for a user's image, accounting for prior knowledge such as her active vocabularies or writing style in her previous documents. As applications of personalized image captioning, we solve two post automation tasks in social networks: hashtag prediction and post generation. The hashtag prediction predicts a list of hashtags for an image, while the post generation creates a natural post text consisting of normal words, emojis, and even hashtags...
April 10, 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Xiao Xie, Xiwen Cai, Junpei Zhou, Nan Cao, Yingcai Wu
Interactive visualization of large image collections is important and useful in many applications, such as personal album management and user profiling on images. However, most prior studies focus on using low-level visual features of images, such as texture and color histogram, to create visualizations without considering the more important semantic information embedded in images. This paper proposes a novel visual analytic system to analyze images in a semantic-aware manner. The system mainly comprises two components: a semantic information extractor and a visual layout generator...
May 15, 2018: IEEE Transactions on Visualization and Computer Graphics
Kun Fu, Jin Li, Junqi Jin, Changshui Zhang
Image captioning aims to generate natural language sentences to describe the salient parts of a given image. Although neural networks have recently achieved promising results, a key problem is that they can only describe concepts seen in the training image-sentence pairs. Efficient learning of novel concepts has thus been a topic of recent interest to alleviate the expensive manpower of labeling data. In this paper, we propose a novel method, Image-Text Surgery, to synthesize pseudoimage-sentence pairs. The pseudopairs are generated under the guidance of a knowledge base, with syntax from a seed data set (ie, MSCOCO) and visual information from an existing large-scale image base (ie, ImageNet)...
April 5, 2018: IEEE Transactions on Neural Networks and Learning Systems
Sachin Muralidhara, Michael J Paul
BACKGROUND: Social media provides a complementary source of information for public health surveillance. The dominate data source for this type of monitoring is the microblogging platform Twitter, which is convenient due to the free availability of public data. Less is known about the utility of other social media platforms, despite their popularity. OBJECTIVE: This work aims to characterize the health topics that are prominently discussed in the image-sharing platform Instagram, as a step toward understanding how this data might be used for public health research...
June 29, 2018: JMIR Public Health and Surveillance
M Kaiser, M Jacobson, P H Andersen, P Bækbo, J J Cerón, J Dahl, D Escribano, S Jacobsen
The original article [1] contains an error whereby the caption in Figure 8 is incorrect; the correct caption can be seen ahead alongside its respective image.
June 1, 2018: BMC Veterinary Research
Toshiya Miyatsu, Reshma Gouravajhala, Robert M Nosofsky, Mark A McDaniel
Learning naturalistic categories, which tend to have fuzzy boundaries and vary on many dimensions, can often be harder than learning well defined categories. One method for facilitating the category learning of naturalistic stimuli may be to provide explicit feature descriptions that highlight the characteristic features of each category. Although this method is commonly used in textbooks and classrooms, theoretically it remains uncertain whether feature descriptions should advantage learning complex natural-science categories...
April 26, 2018: Journal of Experimental Psychology. Learning, Memory, and Cognition
Caio Rodrigues-Silva, Ricardo A R Monteiro, Márcia Dezotti, Adrián M T Silva, Eugénia Pinto, Rui A R Boaventura, Vítor J P Vilar
In the present work, a facile method to prepare translucent anatase thin films on cellulose acetate monolithic (CAM) structures was developed. A simple sol-gel method was applied to synthesize photoactive TiO2 anatase nanoparticles using tetra-n-butyl titanium as precursor. The immobilization of the photocatalyst on CAM structures was performed by a simple dip-coating method. The translucent anatase thin films allow the UV light penetration through the CAM internal walls. The photocatalytic activity was tested on the degradation of n-decane (model volatile organic compound-VOC) in gas phase, using a tubular lab-scale (irradiated by simulated solar light) and pilot-scale (irradiated by natural solar light or UVA light) reactors packed with TiO2 -CAM structures, both equipped with compound parabolic collectors (CPCs)...
April 26, 2018: Environmental Science and Pollution Research International
An Tang, Roger Tam, Alexandre Cadrin-Chênevert, Will Guest, Jaron Chong, Joseph Barfett, Leonid Chepelev, Robyn Cairns, J Ross Mitchell, Mark D Cicero, Manuel Gaudreau Poudrette, Jacob L Jaremko, Caroline Reinhold, Benoit Gallix, Bruce Gray, Raym Geis
Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition...
May 2018: Canadian Association of Radiologists Journal, Journal L'Association Canadienne des Radiologistes
Ruth C Fong, Walter J Scheirer, David D Cox
Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain...
March 29, 2018: Scientific Reports
Airi Nishimi, Takeo Isozaki, Shinichiro Nishimi, Sho Ishii, Takahiro Tokunaga, Hidekazu Furuya, Kuninobu Wakabayashi, Tsuyoshi Kasama
The original version of this article, unfortunately, contained errors. Figure citation, caption, image and updated sentence in the Result section are now presented correctly in this article.
April 2018: Clinical Rheumatology
Leiquan Wang, Xiaoliang Chu, Weishan Zhang, Yiwei Wei, Weichen Sun, Chunlei Wu
Image captioning with a natural language has been an emerging trend. However, the social image, associated with a set of user-contributed tags, has been rarely investigated for a similar task. The user-contributed tags, which could reflect the user attention, have been neglected in conventional image captioning. Most existing image captioning models cannot be applied directly to social image captioning. In this work, a dual attention model is proposed for social image captioning by combining the visual attention and user attention simultaneously...
February 22, 2018: Sensors
Michelle Teti, Deana Hayes, Rose Farnan, Victoria Shaffer, Mary Gerkovich
Adherence to antiretroviral medication among people living with HIV (PL-HIV) is critical to individual and public health. By some estimates only a quarter of PL-HIV are sufficiently adherent, underscoring a continued need for adherence-promoting strategies. In this analysis we explore the effect of adherence education posters developed via Photovoice. A group of PL-HIV generated images and captions to describe their adherence experiences and used their photo-stories to design 10 posters. We assessed viewers' ( N = 111) adherence knowledge, self-efficacy, and communication changes quantitatively and qualitatively before and 3 months after poster placement in the clinic...
July 2018: Health Promotion Practice
(no author information available yet)
In issue 8(5) of JPAH, in Tudor-Locke C, Johnson WD, & Katzmarzyk PT, U.S. Population Profile of Time-Stamped Accelerometer Outputs: Impact of Wear Time ( ), the publisher incor-rectly placed Figures 1 & 2. The image that appears with the Figure 1 caption should appear with the Figure 2 caption; likewise, the image that appears with the Figure 2 caption should appear with the Figure 1 caption. The publisher regrets this error and encourages readers to view the correct placement of each Figure in the version of the article published online...
November 2011: Journal of Physical Activity & Health
Ching-Hui Chen, Vishal M Patel, Rama Chellappa, Ching-Hui Chen, Vishal M Patel, Rama Chellappa, Vishal M Patel, Ching-Hui Chen, Rama Chellappa
Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance...
July 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Arnau Ramisa, Fei Yan, Francesc Moreno-Noguer, Krystian Mikolajczyk
Building upon recent Deep Neural Network architectures, current approaches lying in the intersection of Computer Vision and Natural Language Processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images...
May 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Michelle Teti, Victoria Shaffer, Wilson Majee, Rose Farnan, Mary Gerkovich
Medication adherence is essential to promote the health of people living with HIV (PL-HIV) and prevent HIV transmission in the U.S. Novel medication health promotion interventions are needed that address patient-centeredness, understandability, and communication with providers. The aims of this article are to define the systematic stages we used to develop an effective health promotion intervention via the products (e.g. images and stories) of Photovoice. We designed an intervention to improve HIV adherence knowledge, attitudes, and communication with providers through Photovoice...
June 21, 2017: Health Promotion International
Qi Wu, Chunhua Shen, Peng Wang, Anthony Dick, Anton van den Hengel
Much of the recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we first propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering...
June 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Julia Mabry, Paige E Farris, Vanessa A Forro, Nancy E Findholt, Jonathan Q Purnell, Melinda M Davis
Despite increasing recognition of the role nutrition plays in the health of current and future generations, many women struggle to eat healthy. We used the PhotoVoice method to engage 10 rural women in identifying perceived barriers and facilitators to healthy eating in their homes and community. They took 354 photographs, selected and wrote captions for 62 images, and explored influential factors through group conversation. Using field notes and participant-generated captions, the research team categorized images into factors at the individual, relational, community/organizational, and societal levels of a socioecological model...
January 2016: Global Qualitative Nursing Research
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