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Author: Qi Wu Publisher: Springer Nature ISBN: 9811909644 Category : Computers Languages : en Pages : 238
Book Description
Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc. Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.
Author: IEEE Staff Publisher: ISBN: 9781728150550 Category : Languages : en Pages :
Book Description
ICDAR is a very successful and flagship conference series, which is the biggest and premier international gathering for researchers, scientist and practitioners in the document analysis community
Author: Qi Wu Publisher: Springer Nature ISBN: 9811909644 Category : Computers Languages : en Pages : 238
Book Description
Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc. Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.
Author: Wallace Nutting Publisher: Legare Street Press ISBN: 9781017423907 Category : History Languages : en Pages : 0
Book Description
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Author: Hammad Abdullah Ayyubi Publisher: ISBN: Category : Languages : en Pages : 48
Book Description
Visual Question Answering (VQA) is the task of answering questions based on an image. The field has seen significant advances recently, with systems achieving high accuracy even on open-ended questions. However, a number of recent studies have shown that many of these advanced systems exploit biases in datasets, text of the question or similarity of images in the dataset. To study these reported biases, proposed approaches seek to identify areas of images or words of the questions as evidence that the model focuses on while answering questions. These mechanisms often tend to be limited as the model can answer incorrectly while focusing on the correct region of the image or vice versa. In this thesis, we seek to incorporate and leverage human reasoning to improve interpretability of these VQA models. Essentially, we train models to generate human-like language as evidence or reasons/rationales for the answers that they predict. Further, we show that this type of system has the potential to improve the accuracy on VQA task itself as well.
Author: Andrea Vedaldi Publisher: Springer ISBN: 9783030585259 Category : Computers Languages : en Pages : 803
Book Description
The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Author: Jialin Wu (Ph. D.) Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Visual question answering (VQA) has recently emerged as a challenging multi-modal task and has gained popularity. The goal is to answer questions that query information associated with the visual content in the given image. Since the required information could be from both inside and outside the image, common types of visual features, such as object and attribute detection, fail to provide enough materials for answering the questions. External information, such as captions, explanations, encyclopedia articles, and commonsense databases, can help VQA systems comprehensively understand the image, reason following the right path, and access external facts. Specifically, they provide concise descriptions of the image, precise reasons for the correct answer, and factual knowledge beyond the image. In this dissertation, we present our work on generating image captions that are targeted to help answer a specific visual question. We use explanations to recognize the critical objects to prevent the VQA models from taking language prior shortcuts. We introduce an approach that generates textual explanations and utilizes them to determine which answer is mostly supported. At last, we explore retrieving and exploiting external knowledge beyond the visual content, which is indispensable, to help answer knowledge-based visual questions
Author: IEEE Staff Publisher: ISBN: 9781728111445 Category : Languages : en Pages :
Book Description
T4E 2018 will provide a forum to bring together educators and technology experts interested in promoting learning and teaching through the use of technology Proposals are invited from students, teachers and researchers in academia and industry to present results of their research and development efforts in education through the use of technology and discuss future directions
Author: IEEE Staff Publisher: ISBN: 9781665445979 Category : Languages : en Pages :
Book Description
JCNN is the premier international conference on neural networks theory, analysis, and a wide range of applications IJCNN 2021 is a truly interdisciplinary event with a broad range of contributions on recent advances in neural networks, including neuroscience and cognitive science, computational intelligence and machine learning, hybrid techniques, nonlinear dynamics and chaos, various soft computing technologies, bioinformatics and biomedicine, and engineering applications
Author: Qiongjie Tian Publisher: ISBN: Category : Image processing Languages : en Pages : 0
Book Description
Visual Question Answering (VQA) is a new research area involving technologies ranging from computer vision, natural language processing, to other sub-fields of artificial intelligence such as knowledge representation. The fundamental task is to take as input one image and one question (in text) related to the given image, and to generate a textual answer to the input question. There are two key research problems in VQA: image understanding and the question answering. My research mainly focuses on developing solutions to support solving these two problems. In image understanding, one important research area is semantic segmentation, which takes images as input and output the label of each pixel. As much manual work is needed to label a useful training set, typical training sets for such supervised approaches are always small. There are also approaches with relaxed labeling requirement, called weakly supervised semantic segmentation, where only image-level labels are needed. With the development of social media, there are more and more user-uploaded images available on-line. Such user-generated content often comes with labels like tags and may be coarsely labelled by various tools. To use these information for computer vision tasks, I propose a new graphic model by considering the neighborhood information and their interactions to obtain the pixel-level labels of the images with only incomplete image-level labels. The method was evaluated on both synthetic and real images. In question answering, my research centers on best answer prediction, which addressed two main research topics: feature design and model construction. In the feature design part, most existing work discussed how to design effective features for answer quality / best answer prediction. However, little work mentioned how to design features by considering the relationship between answers of one given question. To fill this research gap, I designed new features to help improve the prediction performance. In the modeling part, to employ the structure of the feature space, I proposed an innovative learning-to-rank model by considering the hierarchical lasso. Experiments with comparison with the state-of-the-art in the best answer prediction literature have confirmed that the proposed methods are effective and suitable for solving the research task.
Author: Sudhakar Reddy Peddinti Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 43
Book Description
Image question answering has gained huge popularity in recent years due to advancements in Deep Learning technologies and computer processing hardware which are able to achieve higher accuracies with faster processing capabilities. Processing image details over natural language information is one of the most challenging tasks in Artificial Intelligence. Most recently, there has been tremendous interest in both creating datasets and proposing deep neural network models for addressing the problem of learning both the images and text information through a question-answering task called Visual Question Answering (VQA). VQA gets us a level closer in terms of human computer interaction through AI. However, VQA is limited in terms of capturing attention only to a certain extent in image (attributes) instead of understanding the semantics of the context in images. In this thesis, we propose a semantic framework known as Context VQA (CVQA) that aims to extend the existing VQA models in two aspects. First, we built a contextual model for defining the semantics of similar contexts from a multi-image set instead of a single image. In the CVQA framework, a two-stage model was proposed (1) to identify one or more images by mapping the semantic sense of the question to the contextual model built from similar contexts of the images; (2) for the select images, provide the appropriate answer for a given question based on the proposed contextual model. Second, CVQA is an enhancement of one of the VQA implementations (VGG-16), which is extended with a more complex model like ResNet-152, and we analyzed the performance of our CVQA framework on 3 datasets – DAQUAR, VQA version1, and VQA version2. From our experiments, we gained improvement in accuracy and runtime. We also present a CVQA application for context-based visual question answering. The proposed enhancements and architecture is implemented on a deep learning platform called TensorFlow, an open source deep learning platform and library developed by Google. Tensorflow library contains many useful pre-implemented building block networks that can make use of parallel GPU computations. To evaluate the performance of the multi image QA, there is no standard dataset. Due to lack of multi-image dataset, we are evaluating the phase-1 using qualitative evaluation and the phase-2 on the benchmark dataset called Visual Question Answering (VQA) and comparing the results with state-of-the art neural network model.
Author: Qi Wu
Author: IEEE Staff
Author: Qi Wu
Author: Wallace Nutting
Author: Hammad Abdullah Ayyubi
Author: Andrea Vedaldi
Author: Jialin Wu (Ph. D.)
Author: IEEE Staff
Author: IEEE Staff
Author: Qiongjie Tian
Author: Sudhakar Reddy Peddinti