VISUAL QUESTION ANSWERING


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Visual Question Answering is a research area about building a computer system to answer questions presented in an image and a natural language. First of all, let’s examine three datasets in Visual Question Answering.

Visual Question Answering (VQA) is an AI-complete task lying at the intersection of computer vision (CV) and natural language processing (NLP). Given an image-question pair, our model generates not only an answer, but also a set of reasons (as text) and visual attention maps.

VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer.

Analyzing the behavior of visual question answering models
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Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60- 70%. In this paper we propose systematic methods to analyze the behavior of these models

An analysis of visual question answering algorithms
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In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 201 they all have flaws in both their content and the way algorithms are evaluated on them. As a result

Visual question answering : A survey of methods and datasets
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Abstract Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual

Fvqa: Fact-based visual question answering
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Visual Question Answering (VQA) has attracted much attention in both computer vision and natural language processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the

Structured attentions for visual question answering
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Visual attention, which assigns weights to image regions according to their relevance to a question is considered as an indispensable part by most Visual Question Answering models. Although the questions involve complex relations among multiple regions, few

In defense of grid features for visual question answering
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Popularized asbottom-upattention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for vision and language tasks like visual question answering (VQA). However, it is not clearVisual question answering (VQA) is an interesting learning setting for evaluating the abilities and shortcomings of current systems for image understanding. Many of the recently proposed VQA systems include attention or memory mechanisms designed to perform

Making the v in vqa matter: Elevating the role of image understanding in visual question answering
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Problems at the intersection of vision and language are of significant importance both as challenging research questions and for the rich set of applications they enable. However, inherent structure in our world and bias in our language tend to be a simpler signal for

Graph-structured representations for visual question answering
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This paper proposes to improve visual question answering (VQA) with structured representations of both scene contents and questions. A key challenge in VQA is to require joint reasoning over the visual and text domains. The predominant CNN/LSTM-based

Learning to reason: End-to-end module networks for visual question answering
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Natural language questions are inherently compositional, and many are most easily answered by reasoning about their decomposition into modular sub-problems. For example, to answer is there an equal number of balls and boxes we can look for balls, look for

Visual question answering with question representation update (qru)
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Our method aims at reasoning over natural language questions and visual images. Given a natural language question about an image, our model updates the question representation iteratively by selecting image regions relevant to the query and learns to give the correct

Visual question answering : Datasets, algorithms, and future challenges
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Abstract Visual Question Answering (VQA) is a recent problem in computer vision and natural language processing that has garnered a large amount of interest from the deep learning, computer vision, and natural language processing communities. In VQA, an

Multi-level attention networks for visual question answering
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Inspired by the recent success of text-based question answering visual question answering (VQA) is proposed to automatically answer natural language questions with the reference to a given image. Compared with text-based QA, VQA is more challenging because the

Visual question generation as dual task of visual question answering
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Visual question answering (VQA) and visual question generation (VQG) are two trending topics in the computer vision, but they are usually explored separately despite their intrinsic complementary relationship. In this paper, we propose an end-to-end unified model, the

Mutan: Multimodal tucker fusion for visual question answering
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Bilinear models provide an appealing framework for mixing and merging information in Visual Question Answering (VQA) tasks. They help to learn high level associations between question meaning and visual concepts in the image, but they suffer from huge

Rubi: Reducing unimodal biases for visual question answering
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Abstract Visual Question Answering (VQA) is the task of answering questions about an image. Some VQA models often exploit unimodal biases to provide the correct answer without using the image information. As a result, they suffer from a huge drop in performance

Vqa: Visual question answering
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We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the

Where to look: Focus regions for visual question answering
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We present a method that learns to answer visual questions by selecting image regions relevant to the text-based query. Our method maps textual queries and visual features from various regions into a shared space where they are compared for relevance with an inner

Simple baseline for visual question answering
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We describe a very simple bag-of-words baseline for visual question answering . This baseline concatenates the word features from the question and CNN features from the image to predict the answer. When evaluated on the challenging VQA dataset , it shows

Tips and tricks for visual question answering : Learnings from the challenge
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This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the VQA Challenge. VQA is a task of significant importance for research in artificial intelligence, given its multimodal nature, clear evaluation protocol, and

Visual Question Answering VQA arXiv
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by A Agrawal 2905 As we demonstrate, a richer variety of visual concepts emerge from visual questions and their answers. 3 VQA DATASET COLLECTION. We now describe the Visual

An Analysis of Visual Question Answering Algorithms CVF
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by K Kafle 141 In visual question answering (VQA), an algorithm must answer text-based questions about images. While multi- ple datasets for VQA have been created since 9 pages

Scene Text Visual Question Answering CVF Open Access
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by AF Biten 55 To this end, in this work we propose a new dataset, called. Scene Text Visual Question Answering (ST-VQA) where the questions and answers are attained in a way 11 pages

VQA: Visual Question Answering The Computer Vision
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by S Antol 2905 Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions se- lectively target 9 pages

Visual Question Answering SJSU ScholarWorks
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by P Kansara bine NLP with computer vision to accomplish the task of Visual Question Answering. (VQA), which is to build a system that can answer natural language

Visual Question Answering on Image Sets ECVA | European
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by A Bansal 5 Answering natural-language questions about images requires understanding both linguistic and visual data. Since its introduction , Visual Question Answering. 16 pages

Learning to Select Question-Relevant Relations for Visual
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by J Lee VQA (visual question answering) is a task that aims to output an answer for a given question re- lated to a given image. 10 pages

Hierarchical Question-Image Co-Attention
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by J Lu 1128 A number of recent works have proposed attention models for Visual Question. Answering (VQA) that generate spatial maps highlighting image regions relevant 9 pages

Reducing Unimodal Biases for Visual Question Answering
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by R Cadene 89 Visual Question Answering (VQA) is the task of answering questions about an image. Some VQA models often exploit unimodal biases to provide the

Visual Question Answering VQA arXiv
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by A Agrawal 2905 As we demonstrate, a richer variety of visual concepts emerge from visual questions and their answers. 3 VQA DATASET COLLECTION. We now describe the Visual

An Analysis of Visual Question Answering Algorithms CVF
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by K Kafle 141 In visual question answering (VQA), an algorithm must answer text-based questions about images. While multi- ple datasets for VQA have been created since 9 pages

VQA: Visual Question Answering The Computer Vision
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by S Antol 2905 Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions se- lectively target 9 pages

Visual Question Answering SJSU ScholarWorks
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by P Kansara bine NLP with computer vision to accomplish the task of Visual Question Answering. (VQA), which is to build a system that can answer natural language

Reducing Unimodal Biases for Visual Question Answering
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by R Cadene 89 Visual Question Answering (VQA) is the task of answering questions about an image. Some VQA models often exploit unimodal biases to provide the correct. 12 pages

Hierarchical Question-Image Co-Attention
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by J Lu 1128 A number of recent works have proposed attention models for Visual Question. Answering (VQA) that generate spatial maps highlighting image regions relevant 9 pages

Overcoming Language Priors in Visual Question Answering
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by S Ramakrishnan 98 Modern Visual Question Answering (VQA) models have been shown to rely heavily on superficial correlations between question and answer words learned. 11 pages

Explicit Knowledge-based Reasoning for Visual Question
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by P Wang 132 Visual Question Answering (VQA) methods aim to interac- tively answer questions about images. The questions are typi- cally posed in natural language, as are 7 pages

Multimodal Learning and Reasoning for Visual Question
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by I Ilievski 36 The visual question answering (VQA), a task to provide answers to natural language questions about the contents of an image, has been proposed to fill this gap 12 pages

Self-Critical Reasoning for Robust Visual Question Answering
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Visual Question Answering (VQA) deep-learning systems tend to capture superfi- cial statistical correlations in the training data because of strong language 11 pages

Visual Question Answering on Image Sets ECVA | European
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by A Bansal 5 Answering natural-language questions about images requires understanding both linguistic and visual data. Since its introduction , Visual Question Answering. 16 pages

Using Explanations to Improve Ensembling of Visual Question
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by NF Rajani 11 Visual Question Answering (VQA) is a challenging task that requires systems to attend to regions of an image or question or both for producing an output.

Visual Question Answering
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by LW Chen 1 Each question is answered by 10 human annotators. 11. VQA dataset. Antol, Stanislaw, et al. Vqa: Visual question answering. Proceedings of the IEEE 80 pagesby Y Qiu 2 Illustration of proposed multi-view Visual Question An- swering. Conventional VQA methods reply an answer against a linguistic question about a given RGB image.

Question Type Guided Attention in Visual Question Answering
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by Y Shi 32 Visual Question Answering (VQA) requires integration of feature maps with drastically different structures. Image descriptors have structures at mul-.

Answer-Type Prediction for Visual Question Answering
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by K Kafle 105 tem capable of answering open-ended text-based questions about images, which is known as Visual Question Answer- ing (VQA). Our approachs key insight is

NLM at VQA-Med 2020: Visual Question Answering and
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by M Sarrouti 2 Library of Medicine (NLM) in Visual Question Answering (VQA) and. Visual Question Generation (VQG) tasks of the VQA-Med challenge at. ImageCLEF 2020.

Learning to Select Question-Relevant Relations for Visual
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by J Lee VQA (visual question answering) is a task that aims to output an answer for a given question re- lated to a given image.

Focal Visual-Text Attention for Visual Question Answering
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by J Liang 75 Visual question answer- ing (VQA) is a successful direction utilizing both com- puter vision and natural language processing techniques to solve an

A Training Paradigm for Out-of-Distribution Generalization in
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by T Gokhale 19 Visual question answering (VQA) (Antol et al.,. 2015) is a task at the crucial intersection of vision and language. The aim of VQA models is to

Visual Question Answering via Dense Captioning Stanford
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In recent years,Visual Question Answering (VQA) (1) has become a prominent research problem at the intersection of natural language processing and computer.

Semantically Guided Visual Question Answering
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by H Zhao 7 task of Visual Question Answering (VQA) by incorporating and enriching semantic knowledge in a VQA model. We first apply Multiple Instance Learning (MIL) to

Multi-level Attention Networks for Visual Question Answering
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by D Yu 187 Inspired by the recent success of text-based question an- swering, visual question answering (VQA) is proposed to automatically answer natural language

Visual Question Answering with Question Representation
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by R Li 73 learns to give the correct answer. Our model contains several reasoning layers, exploiting complex visual relations in the visual question answering (VQA)

OCR-VQA: Visual Question Answering by Reading Text in
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by A Mishra 31 Abstract The problem of answering questions about an image is popularly known as visual question answering (or. VQA in short). It is a well-established

Visual Question Answering: Datasets, Methods, Challenges
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fields of AI is the Visual Question Answering, or VQA. In this task, the input is an image and an open-ended ques- tion about the image, and the output is

Visual Question Answering on 360 Images
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We introduce VQA 360⠦, a novel task of visual question answering on world image-question-answer triplets for a variety of ques- tion types.

Co-attention Spatial Reasoning for Visual Question Answering
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by Y Qin Visual question answering (VQA) requires both inferring and providing correct answers based on the semantics of the question and the content of the image. We

VQA-Med: Overview of the Medical Visual Question
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by AB Abacha 68 VQA-Med: Overview of the Medical Visual. Question Answering Task at ImageCLEF 2019. Asma Ben Abacha Sadid A. Hasan Vivek V. Datla Joey Liu Dina.

Visual Question Answering Sofiya Semenova
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by S Semenova The problem of Visual Question Answering. (VQA) offers a difficult challenge to the fields of computer vision (CV) and natural language processing (NLP) it is

Structured Attentions for Visual Question Answering People
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Visual Question Answering (VQA) is a comprehensive task inspecting intelligent systems ability to recognize im- ages and natural languages together. Advances

Improving Visual Question Answering using Active Perception
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by T Bozinis 1 Abstract Visual Question Answering (VQA) is one of the most challenging emerging applications of deep learning. Providing powerful attention mechanisms is

Found a Reason for me Weakly-supervised Grounded Visual
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vised visual grounding based on VQA supervision. The idea here is that both tasks, the visual question-answering as well as the correct visual grounding,

Multi-View Visual Question Answering with Active Viewpoint
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by Y Qiu 5 Conventional visual question answering (VQA) methods are designed to answer given questions based on single-view images.

An Effective Dense Co-Attention Networks for Visual Question
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by S He 3 Visual Question Answering (VQA) is a multimodal research task that aims to answer questions related to the given image. Compared with other

Medical Visual Question Answering via Conditional Reasoning
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by LM Zhan 10 Medical visual question answering (Med-VQA) aims to accurately answer a clinical question presented with a medical image. Despite its enormous

RSVQA Meets BigEarthNet: A New, Large-Scale, Visual
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by S Lobry RSVQA MEETS BIGEARTHNET: A NEW, LARGE-SCALE, VISUAL QUESTION. ANSWERING DATASET FOR REMOTE SENSING. Sylvain Lobry Begüm Demir Devis Tuia3.

Multimodal Cross-guided Attention Networks for Visual
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by H Liu 1 Keywords visual question answering; attention; cross-guided; gated activation. I. INTRODUCTION. VQA is a multimodal joint learning task of AI-complete.

VQA: Visual Question Answering
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by S Antol 2905 Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions se- lectively target

An Improve Attention based Architecture for Visual Question
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by T Rahman Visual question answer- ing (VQA) is one of the most challenging tasks among all multimodal based learning tasks that re- quires both image and textual

Lightweight Visual Question Answering using Scene Graphs
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Visual question answering (VQA) is a challenging problem in ma- a textual question encoder to generate answers through question- graph conditioning.

Visual Question Answering using Explicit Visual Attention
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by V Lioutas 3 faced today is Visual Question Answering (VQA), which requires a machine to properly understand a question about a reference visual input, expressed in

R-VQA: Learning Visual Relation Facts with Semantic
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by P Lu 39 Recently, Visual Question Answering (VQA) has emerged as one of the most significant tasks in multimodal learning as it requires understanding both visual and

Passage Retrieval for Outside-Knowledge Visual Question
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Recent work on Question Answering (QA) [ 3 44] mostly fo- cuses on uni-modal information needs, i.e., text- or voice-based questions (voice input can be

Semantic Equivalent Adversarial Data Augmentation for
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by R Tang 8 Visual Question Answering (VQA) has achieved great suc- cess thanks to the fast development of deep neural networks (DNN). On.

Learning Answer Embeddings for Visual Question Answering
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by H Hu 20 Visual question answering (Visual QA) has made signif- icant progress in the last few years. More than 10 datasets have been released for the task [ 1 26],

arXiv:1907.00490v1 [cs.CV] 30 Computer Vision
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by AF Biten 26 Abstract This paper presents final results of ICDAR 2019. Scene Text Visual Question Answering competition (ST-VQA).

learning to count objects in natural images for visual
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by Y Zhang 134 Visual Question Answering (VQA) models have struggled with counting objects in natural images so far. We identify a fundamental problem due to softby SH Chou 4 We introduce VQA 360⠦, a novel task of visual question answering on world image-question-answer triplets for a variety of ques- tion types.

Dual Attention Network for Visual Question Answering CS
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by H Xu 3 The selected linguistic and visual features are combined by a recurrent model to infer the final answer. We experiment with different question repre- sentations

Revisiting Visual Question Answering Baselines ECCV 2016
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by A Jabri 239 Study of Visual Question Answering has led to the exploration of complex models designed to do multi-modal reasoning and attention.

VQA: Visual Question Answering
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by S Antol 2905 >250K images. ⠋ 200K from MS COCO. □ 80 train / 40 val / 80 test. ⠋ 50K from Abstract ⠋ QAs. ⠋ 3 questions/image. ⠋ 10 answers/question.

Overview of the VQA-Med Task at ImageCLEF 2020: Visual
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by AB Abacha 27 For the visual question answering task, similar to 201 given a radiology medical image accompanied by a clinically relevant question,

Dynamic Memory Networks for Visual and Textual Question
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by C Xiong 665 Neural network architectures with memory and attention mechanisms exhibit certain reason- ing capabilities required for question answering.

iVQA: Inverse Visual Question Answering Informatics
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by F Liu 20 Image captioning. [31], visual question answering , natural language object retrieval [20] and visual Turing tests provide multi-.

KVQA: Knowledge-aware Visual Question Answering Indian
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by S Shah 46 Visual Question Answering (VQA) has emerged as an impor- tant problem spanning Computer Vision, Natural Language. Processing and Artificial Intelligence

A Survey On Visual Questioning Answering : Datasets
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Visual Question Answering (VQA) is one of the complex computer vision task which combines techniques from other computer vision tasks to improve interaction

Visual question answering (VQA) in a toy world
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1 referred as Visual Question Answering and has been very popular in computer Given an image, VQA systems try to answer an input question.

DualNet: Domain-Invariant Network for Visual Question
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by K Saito 54 DUALNET: DOMAIN-INVARIANT NETWORK FOR VISUAL QUESTION ANSWERING. Kuniaki Saito, Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada. ∗. The University of Tokyo.

Overview of ImageCLEF Medical Domain Visual Sadid
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by SA Hasan 48 Inspired by the recent success of visual question an- swering in the general domain, a pilot task was proposed this year to focus on visual question answering

Visual Question Generation as Dual Task of Visual Yikang Li
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VQA refers to answering question based on the image, while VQG aims at generating reasonable questions given the image content and an answer. Both VQA and VQG

A Deep Interactive Framework for Post-Disaster Management
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by A Sarkar Visual Question Answering: A Deep Interactive Framework for Post-Disaster. Management and Damage Assessment. Argho Sarkar 1 Maryam Rahnemoonfar 1. Abstract

Learning to Recognize Visual Concepts for Visual Question
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by D Gao Abstract Solving visual question answering (VQA) task re- quires recognizing many diverse visual concepts as the answer. These visual concepts contain rich

Double-Layer Affective Visual Question Answering Network
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by Z Guo 1 fect, we propose the Double-Layer Affective Visual Question Answering Network. (DAVQAN) that divides the task of generating emotional answers in VQA into

Pointer-CNN for Visual Question Answering
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by J Svidt Visual Question Answering(VQA) is an interesting problem from a research per- spective, as it is an intersection of the Computer Vision and

Using similarity based image caption to aid visual question
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by J Kang Visual Question Answering (VQA) and image captioning are tasks that require understanding of the features of images and linguistic features

An Efficient Visual based Question Answering System using
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by D Saranya 3 perform the Visual Question Answering (VQA) mission, which is to construct systems that can respond to image questions in natural language.

Overcoming Priors for Visual Question Answering ECE
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by A Agrawal 281 Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call. Visual Question Answering under Changing Priors (VQA-. CP v1 and VQA-CP v2

Visual Question Answering with External Knowledge SSRN
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by S Voruganti Visual Question Answering (VQA) system is an algorithm that takes an image, and a natural language question about it as input and produces an.

Fast Parameter Adaptation for Few-shot Image Captioning
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by X Dong 24 Visual Question. Answering: given an open-ended question about an image, the model should generate a natural language answer. 1 INTRODUCTION. It is challenging

Reciprocal Attention Fusion for Visual Question Answering
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by MR Farazi 11 Existing attention mechanisms either attend to local image-grid or object level fea- tures for Visual Question Answering (VQA). Motivated by the observation

IQA: Visual Question Answering in Interactive Environments
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by D Gordon 235 We introduce InteractiveQuestionAnswering (IQA), the task of answering questions that require an autonomous agent to interact with a dynamic visual

Visual Question Answering 2.0 Image Processing Group
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by FR Sánchez This bachelors thesis explores different deep learning techniques to solve the Visual Question-. Answering (VQA) task, whose aim is to

VQA: Visual Question Answering UCSD CSE
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Visual Question Answering. Given an input image and a natural-language question about the image, the task is to provide a natural-language answer as output.

A Novel Approach on Visual Question Answering by Dialnet
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by S Jha 12 understanding to solve the problem of Visual Question Answering by the machine learning. Recently, deep learning architectures constructed.

An Empirical Study on Leveraging Scene Graphs for Visual
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Visual question answering (Visual QA) has attracted significant attention these years. While a variety of algorithms have been proposed, most of them are

Image Question Answering: A Review IRJET
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Visual Question Answering (VQA) problem are diverse and arbitrary in the sense that they not only answer questions to detect an object in the image (What

Non-monotonic Logical Reasoning and Deep Learning for
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by H Riley 7 Explainable Visual Question Answering. Heather Riley. Electrical and Computer Engineering. The University of Auckland, New Zealand hril230@aucklanduni.ac.nz.

Question Relevance in Visual Question Answering arXiv
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by P Prabhakar Abstract Free-form and open-ended Visual Question Answering systems solve the problem of providing an accurate natural language an-.

VQA: Visual Question Answering
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Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Both the questions and answers are

Question-guided hybrid convolution for visual question
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by P GAO 56 Convolution (QGHC) network for Visual Question Answering (VQA). Most state-of-the-art VQA methods fuse the high-level textual and vi-.

arXiv:1804.02088v2 [cs.CV] 18 Caltech Authors
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by Y Shi 32 Visual Question Answering (VQA) requires integration of feature maps with drastically different structures. Image descriptors have structures at mul-.

Explicit Reasoning over End-to-End Neural Architectures for
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by S Aditya 43 Here we adopt Visual Question Answering (VQA) as an example task, where a system is expected to answer a question in natural language about an image.

Visual Question Answering For IIT Kanpur
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Visual Question Answering. 2 Ask Your Neurons. Introduction. Evaluation Metric. 3 Dataset Used. MS-COCO VQA Dataset. 4 Model Configuration.

The Case of Visual Question Answering Inria
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What is Visual Question Answering (aka VQA). The objective of a VQA model combines visual and textual features in order to answer questions grounded in an

Identifying Counterexamples in Visual Question Answering
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by G Grand Visual question answering; counterexamples; multimodal; repre- sentation learning; semantic reasoning; transfer learning. 1 INTRODUCTION.

Insensibility of Question Word Order in Visual Question
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by H Lee of existing Visual Question Answering(VQA) model trained on VQA v2 dataset. We show that randomly shuffling the word order of in- put question has little

Physical Science Paper 1 Exemplar Question
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Years Question Papers with Marking Scheme Toppers Answers for Typology OF Questions Objective Type Questions latest Visual Case Study based

Short Answer Study Guide Questions Great Expectations Key
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using visual memory as a learning tool. It also contains learning objectives, unique study tips, and approximately 000 study questions with an answer

Visual Intelligence How We Create What We See
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Second, I will discuss visual question answering: existing datasets consist primarily of short, simple questions; to study more complex questions requiring

Feasibility, acceptability and appropriateness of a PLOS
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by B Gerstl and 92.7% were willing to answer all questions, with 97% agreeing that anxiety, distress, anger and need for help via a visual ther-.

(COVID-19): Guidance for Intermediate Care Facilities (ICF)
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(Answering yes to this question requires quarantine from other persons in the ICF, regardless of vaccination status.).

The Turing Test: Verbal Behavior as the Hallmark of Intelligence
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Shieber abstracts from incidental properties like visual appearance that might immediately answer the question of which entity is the ma-.

Benefits of the use of ICT in school activities by students with
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by H Lidstr m 54 students with motor, speech, visual, and hearing impairment: a literature review, 201 such as answering and asking questions in class, increased,

later DergiPark
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by P Alavinia 4 Research Questions. To be able to come up with satisfactory answers regarding the overriding arguments of the current study the following research questions

The Basal Ganglia and Adaptive Motor Control JSTOR
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by AM Graybiel 1994 1441 firing and40-Hz oscillations in the visual questions: If information is sent in parallel One possible answer to this question.

DELHI UNIVERSITY ENTRANCE TEST (PG)
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2 (b) Visual Impairement: Blindness Low Vision; for Review for a question without answering it, the corresponding question Marked for.

Application for a Passenger Transport Licence Code Roads
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Please note: If you answer Yes to any question from 4 to 10 you driving eg poor visual acuity, monocular vision, double vision,.

Everyones First Chess Workbook
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As a chess coach, I have answered thousands of questions on every imaginable learning tool as it offers a different dimensional perspective (visual).




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