Images with visual and scene text content are ubiquitous in everyday life. However current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We undertake the task of matching Advertisement images against their human generated statements that describe the action that the ad prompts and the rationale it provides for taking this action. We extract the scene text and generate semantic and lexical text representations, which are used in the interpretation of the Ad Image. To deal with irrelevant or erroneous detection of scene text, we use a text attention scheme. We also learn an embedding of the visual channel, visual features based on detected symbolism and objects, into a semantic embedding space, leveraging text semantics obtained from scene text. We show how the multi channel approach, involving visual semantics and scene text, improves upon the current state of the art.
The ad image dataset presented in @cite_26 proposed multiple task for automated understanding and classification of ad images. In an extension of that work, @cite_4 proposed the task of matching ad images against relevant sentences (see Fig. for an example of this task). Similar to our approach, they embed the images in a joint text-image embedding space, where retrieval is feasible. The embedding scheme consists of features from salient regions proposed by symbol detectors and automated captions generated by Densecap @cite_34 . The text captions, treated as external knowledge, are projected in to word2vec space, and the resultant aggregated semantic vector is augmented with the visual features. Though generated caption gives the image a semantic description, which can be easily compared with the statements for retrieval, this scheme needs externally trained model for generating captions.
@cite_26 @cite_4 @cite_34
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There is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action. We propose the novel problem of automatic advertisement understanding. To enable research on this problem, we create two datasets: an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. Our data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer (What should I do according to this ad, and why should I do it?), and symbolic references ads make (e.g. a dove symbolizes peace). We also analyze the most common persuasive strategies ads use, and the capabilities that computer vision systems should have to understand these strategies. We present baseline classification results for several prediction tasks, including automatically answering questions about the messages of the ads. In order to convey the most content in their limited space, advertisements embed references to outside knowledge via symbolism. For example, a motorcycle stands for adventure (a positive property the ad wants associated with the product being sold), and a gun stands for danger (a negative property to dissuade viewers from undesirable behaviors). We show how to use symbolic references to better understand the meaning of an ad. We further show how anchoring ad understanding in general-purpose object recognition and image captioning can further improve results. We formulate the ad understanding task as matching the ad image to human-generated statements that describe the action that the ad prompts and the rationale it provides for this action. We greatly outperform the state of the art in this task. We also show additional applications of our learned representations for ranking the slogans of ads, and clustering ads according to their topic. We introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning task generalizes object detection when the descriptions consist of a single word, and Image Captioning when one predicted region covers the full image. To address the localization and description task jointly we propose a Fully Convolutional Localization Network (FCLN) architecture that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with a single round of optimization. The architecture is composed of a Convolutional Network, a novel dense localization layer, and Recurrent Neural Network language model that generates the label sequences. We evaluate our network on the Visual Genome dataset, which comprises 94,000 images and 4,100,000 region-grounded captions. We observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings.