2017-05-18
Bilinear models provide an appealing framework for mixing and merginginformation in Visual Question Answering (VQA) tasks. They help to learn highlevel associations between question meaning and visual concepts in the image,but they suffer from huge dimensionality issues. We introduce MUTAN, amultimodal tensor-based Tucker decomposition to efficiently parametrizebilinear interactions between visual and textual representations. Additionallyto the Tucker framework, we design a low-rank matrix-based decomposition toexplicitly constrain the interaction rank. With MUTAN, we control thecomplexity of the merging scheme while keeping nice interpretable fusionrelations. We show how our MUTAN model generalizes some of the latest VQAarchitectures, providing state-of-the-art results.
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2019-01-31

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2018-12-23

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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 reasoning process on visual domain needs both effective semantic embedding and fine-grained visual understanding. Existing approaches predominantly infer answers from the abstract low-level visual features, while neglecting the modeling of high-level image semantics and the rich spatial context of regions. To solve the challenges, we propose a multi-level attention network for visual question answering that can simultaneously reduce the semantic gap by semantic attention and benefit fine-grained spatial inference by visual attention. First, we generate semantic concepts from high-level semantics in convolutional neural networks (CNN) and select those question-related concepts as semantic attention. Second, we encode region-based middle-level outputs from CNN into spatially-embedded representation by a bidirec-tional recurrent neural network, and further pinpoint the answer-related regions by multiple layer perceptron as visual attention. Third, we jointly optimize semantic attention , visual attention and question embedding by a softmax classifier to infer the final answer. Extensive experiments show the proposed approach outperforms the-state-of-arts on two challenging VQA datasets.
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Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The approaches used to represent the images and questions in a fine-grained manner and questions and to fuse these multi-modal features play key roles in performance. Bilinear pooling based models have been shown to outperform traditional linear models for VQA, but their high-dimensional representations and high computational complexity may seriously limit their applicability in practice. For multi-modal feature fusion, here we develop a Multi-modal Fac-torized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilin-ear pooling approaches. For fine-grained image and question representation, we develop a 'co-attention' mechanism using an end-to-end deep network architecture to jointly learn both the image and question attentions. Combining the proposed MFB approach with co-attention learning in a new network architecture provides a unified model for VQA. Our experimental results demonstrate that the single MFB with co-attention model achieves new state-of-the-art performance on the real-world VQA dataset. Code available at https://github.com/yuzcccc/mfb.
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2018-11-30

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2018-06-19

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2019-05-10

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2018-04-03
A key solution to visual question answering (VQA) exists in how to fuse visual and language features extracted from an input image and question. We show that an attention mechanism that enables dense, bi-directional interactions between the two modalities contributes to boost accuracy of prediction of answers. Specifically, we present a simple architecture that is fully symmetric between visual and language representations, in which each question word attends on image regions and each image region attends on question words. It can be stacked to form a hierarchy for multi-step interactions between an image-question pair. We show through experiments that the proposed architecture achieves a new state-of-the-art on VQA and VQA 2.0 despite its small size. We also present qualitative evaluation, demonstrating how the proposed attention mechanism can generate reasonable attention maps on images and questions , which leads to the correct answer prediction.
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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 algorithm needs to answer text-based questions about images. Since the release of the first VQA dataset in 2014, additional datasets have been released and many algorithms have been proposed. In this review, we critically examine the current state of VQA in terms of problem formulation, existing datasets, evaluation metrics, and algorithms. In particular, we discuss the limitations of current datasets with regard to their ability to properly train and assess VQA algorithms. We then exhaustively review existing algorithms for VQA. Finally, we discuss possible future directions for VQA and image understanding research.
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We address the problem of Visual Question Answering (VQA), which requires joint image and language understanding to answer a question about a given photograph. Recent approaches have applied deep image captioning methods based on convolutional-recurrent networks to this problem, but have failed to model spatial inference. To remedy this, we propose a model we call the Spatial Memory Network and apply it to the VQA task. Memory networks are recurrent neural networks with an explicit attention mechanism that selects certain parts of the information stored in memory. Our Spatial Memory Network stores neuron activations from different spatial regions of the image in its memory, and uses the question to choose relevant regions for computing the answer, a process of which constitutes a single "hop" in the network. We propose a novel spatial attention architecture that aligns words with image patches in the first hop, and obtain improved results by adding a second attention hop which considers the whole question to choose visual evidence based on the results of the first hop. To better understand the inference process learned by the network, we design synthetic questions that specifically require spatial inference and visualize the attention weights. We evaluate our model on two published visual question answering datasets, DAQUAR [1] and VQA [2], and obtain improved results compared to a strong deep baseline model (iBOWIMG) which concatenates image and question features to predict the answer [3].
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2016-07-20
Visual Question Answering (VQA) is a challenging task that has receivedincreasing attention from both the computer vision and the natural languageprocessing communities. Given an image and a question in natural language, itrequires reasoning over visual elements of the image and general knowledge toinfer the correct answer. In the first part of this survey, we examine thestate of the art by comparing modern approaches to the problem. We classifymethods by their mechanism to connect the visual and textual modalities. Inparticular, we examine the common approach of combining convolutional andrecurrent neural networks to map images and questions to a common featurespace. We also discuss memory-augmented and modular architectures thatinterface with structured knowledge bases. In the second part of this survey,we review the datasets available for training and evaluating VQA systems. Thevarious datatsets contain questions at different levels of complexity, whichrequire different capabilities and types of reasoning. We examine in depth thequestion/answer pairs from the Visual Genome project, and evaluate therelevance of the structured annotations of images with scene graphs for VQA.Finally, we discuss promising future directions for the field, in particularthe connection to structured knowledge bases and the use of natural languageprocessing models.
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2016-06-06

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2017-07-02
It is commonly assumed that language refers to high-level visual conceptswhile leaving low-level visual processing unaffected. This view dominates thecurrent literature in computational models for language-vision tasks, wherevisual and linguistic input are mostly processed independently before beingfused into a single representation. In this paper, we deviate from this classicpipeline and propose to modulate the \emph{entire visual processing} bylinguistic input. Specifically, we condition the batch normalization parametersof a pretrained residual network (ResNet) on a language embedding. Thisapproach, which we call MOdulated RESnet (\MRN), significantly improves strongbaselines on two visual question answering tasks. Our ablation study shows thatmodulating from the early stages of the visual processing is beneficial.
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Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge.
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