Learning Convolutional Text Representations for Visual Question Answering

Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual representation as compared to other natural language processing tasks. In this work, we perform a detailed analysis on natural language questions in visual question answering. Based on the analysis, we propose to rely on convolutional neural networks for learning textual representations. By exploring the various properties of convolutional neural networks specialized for text data, such as width and depth, we present our "CNN Inception + Gate" model. We show that our model improves question representations and thus the overall accuracy of visual question answering models. We also show that the text representation requirement in visual question answering is more complicated and comprehensive than that in conventional natural language processing tasks, making it a better task to evaluate textual representation methods. Shallow models like fastText, which can obtain comparable results with deep learning models in tasks like text classification, are not suitable in visual question answering.Comment: Conference paper at SDM 2018. https://github.com/divelab/sva

Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions

The key idea of variational auto-encoders (VAEs) resembles that of traditional auto-encoder models in which spatial information is supposed to be explicitly encoded in the latent space. However, the latent variables in VAEs are vectors, which can be interpreted as multiple feature maps of size 1x1. Such representations can only convey spatial information implicitly when coupled with powerful decoders. In this work, we propose spatial VAEs that use feature maps of larger size as latent variables to explicitly capture spatial information. This is achieved by allowing the latent variables to be sampled from matrix-variate normal (MVN) distributions whose parameters are computed from the encoder network. To increase dependencies among locations on latent feature maps and reduce the number of parameters, we further propose spatial VAEs via low-rank MVN distributions. Experimental results show that the proposed spatial VAEs outperform original VAEs in capturing rich structural and spatial information.Comment: Accepted by SDM2019. Code is publicly available at https://github.com/divelab/sva

Deep Attention Networks for Images and Graphs

Deep learning has achieved great success in various machine learning areas, such as computer vision, natural language processing, and graph representation learning. While numerous deep neural networks (DNNs) have been proposed, the set of fundamental building blocks of DNNs remains small, including fully-connected layers, convolutions and recurrent units. Recently, the attention mechanism has shown promise in serving as a new kind of fundamental building blocks. Deep attention networks (DANs), i.e. DNNs that use the attention mechanism as a fundamental building block, have revolutionized the area of natural language processing. However, developing DANs for computer vision and graph representation learning applications is still challenging. Due to the intrinsic differences in data and applications, directly migrating DANs from textual data to images and graphs is usually either infeasible or ineffective. In this dissertation, we address this challenge by analyzing the functionality of the attention mechanism and exploring scenarios where DANs can push the limits of current DNNs. We propose several effective DANs for images and graphs. For images, we build DANs for a variety of image-to-image transformation applications by proposing powerful attention-based building blocks. First, we start the exploration through studying a common problem in dilated convolutions, which naturally results in the use of the attention mechanism. Dilated convolutions, a variant of convolutions, have been widely applied in deep convolutional neural networks (DCNNs) for image segmentation. However, dilated convolutions suffer from the gridding artifacts, which hampers the performance. We propose two simple yet effective degridding methods by studying a decomposition of dilated convolutions, and generalize them by defining separable and shared (SS) operators. Then we connect the SS operators with the attention mechanism and propose the SS output layer, which is able to smooth the entire DCNNs by only replacing the output layer and improves the performance significantly. Second, we notice an interesting fact from the first study that, as the attention mechanism allows the SS output layer to have a receptive field of any size, the best performance is achieved when using a global receptive field. This fact motivates us to think of the attention mechanism as global operators, as opposed to local operators like convolutions. With this insight, we propose the non-local U-Nets, which are equipped with flexible attention-based global aggregation blocks, for biomedical image segmentation. In particular, we are the first to enable the attention mechanism for down-sampling and up-sampling processes. Finally, we go beyond biomedical image segmentation and extend the non-local U-Nets to global voxel transformer networks (GVTNets), which serve as a powerful open-source tool for 3D image-to-image transformation tasks. In addition to leveraging the non-local property of the attention mechanism under the supervised learning setting, we also investigate the generalization ability of the attention mechanism under the transfer learning setting. We perform thorough experiments on a wide range of real-world image-to-image transformation tasks, whose results clearly demonstrate the effectiveness and efficiency of our proposed DANs. For graphs, we develop DANs for both graph and node classification applications. First, we focus on graph pooling, which is necessary for graph neural networks (GNNs) to perform graph classification tasks. In particular, we point out that the second-order pooling naturally satisfies the requirement of graph pooling but encounters practical problems. To overcome these problems, we propose attentional second-order pooling. Specifically, we bridge the second-order pooling with the attention mechanism and design an attention-based pooling method that can be flexibly used as either global or hierarchical graph pooling. Second, on node classification tasks, we pay attention to the problem that most GNNs lack the ability of performing effective non-local aggregation, which greatly limits the performance on disassortative graphs. In particular, it even leads to worse performance of GNNs than simple multi-layer perceptrons on some disassortative graphs. In order to address this problem, we propose a simple yet effective non-local aggregation framework with an efficient attention-guided sorting for GNNs, based on which we develop non-local GNNs. Experimental results on various graph and node classification benchmark datasets show that our DANs improve the performance significantly and consistently

Improved Approximation Ratios of Fixed-Price Mechanisms in Bilateral Trades

We continue the study of the performance for fixed-price mechanisms in the bilateral trade problem, and improve approximation ratios of welfare-optimal mechanisms in several settings. Specifically, in the case where only the buyer distribution is known, we prove that there exists a distribution over different fixed-price mechanisms, such that the approximation ratio lies within the interval of [0.71, 0.7381]. Furthermore, we show that the same approximation ratio holds for the optimal fixed-price mechanism, when both buyer and seller distributions are known. As a result, the previously best-known (1 - 1/e+0.0001)-approximation can be improved to $0.71$. Additionally, we examine randomized fixed-price mechanisms when we receive just one single sample from the seller distribution, for both symmetric and asymmetric settings. Our findings reveal that posting the single sample as the price remains optimal among all randomized fixed-price mechanisms