Relative attribute models can compare images in terms of all detected properties or attributes, exhaustively predicting which image is fancier, more natural, and so on without any regard to ordering. However, when humans compare images, certain differences will naturally stick out and come to mind first. These most noticeable differences, or prominent differences, are likely to be described first. In addition, many differences, although present, may not be mentioned at all. In this work, we introduce and model prominent differences, a rich new functionality for comparing images. We collect instance-level annotations of most noticeable differences, and build a model trained on relative attribute features that predicts prominent differences for unseen pairs. We test our model on the challenging UT-Zap50K shoes and LFW10 faces datasets, and outperform an array of baseline methods. We then demonstrate how our prominence model improves two vision tasks, image search and description generation, enabling more natural communication between people and vision systems.
1em Relative attributes, first introduced in @cite_40 , represent an image's attribute strength with respect to other images @cite_1 @cite_57 @cite_41 @cite_66 @cite_10 @cite_22 @cite_35 @cite_12 @cite_51 @cite_2 @cite_43 , and are a richer representation than binary presence absence. Relative attributes enable between images ( , the left shoe is than the right), and have been used to discern fine-grained differences @cite_44 @cite_22 and predict image virality @cite_1 . Recently, deep CNNs have been used to both predict relative attributes @cite_48 @cite_12 @cite_51 as well as generate synthetic images of varying attribute strengths @cite_5 @cite_14 . However, no prior work considers which relative attributes stand out, or what relative attributes humans tend to use in speech. Our work introduces prominent differences, a novel functionality representing most noticeable differences in the vocabulary of relative attributes.
@cite_35 @cite_14 @cite_22 @cite_41 @cite_48 @cite_1 @cite_57 @cite_43 @cite_40 @cite_44 @cite_2 @cite_51 @cite_5 @cite_10 @cite_66 @cite_12
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We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons. Unlike previous methods, our network jointly learns the attribute’s features, localization, and ranker. The localization module of our network discovers the most informative image region for the attribute, which is then used by the ranking module to learn a ranking model of the attribute. Our end-to-end framework also significantly speeds up processing and is much faster than previous methods. We show state-of-the-art ranking results on various relative attribute datasets, and our qualitative localization results clearly demonstrate our network’s ability to learn meaningful image patches. Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining. In this paper we study viral images from a computer vision perspective. We introduce three new image datasets from Reddit1 and define a virality score using Reddit metadata. We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images, and the dominant topic of a viral image. We also compare machine performance to human performance on these tasks. We find that computers perform poorly with low level features, and high level information is critical for predicting virality. We encode semantic information through relative attributes. We identify the 5 key visual attributes that correlate with virality. We create an attribute-based characterization of images that can predict relative virality with 68.10 accuracy (SVM+Deep Relative Attributes) -better than humans at 60.12 . Finally, we study how human prediction of image virality varies with different “contexts” in which the images are viewed, such as the influence of neighbouring images, images recently viewed, as well as the image title or caption. This work is a first step in understanding the complex but important phenomenon of image virality. Our datasets and annotations will be made publicly available. In recent years, there has been a great deal of progress in describing objects with attributes. Attributes have proven useful for object recognition, image search, face verification, image description, and zero-shot learning. Typically, attributes are either binary or relative: they describe either the presence or absence of a descriptive characteristic, or the relative magnitude of the characteristic when comparing two exemplars. However, prior work fails to model the actual way in which humans use these attributes in descriptive statements of images. Specifically, it does not address the important interactions between the binary and relative aspects of an attribute. In this work we propose a spoken attribute classifier which models a more natural way of using an attribute in a description. For each attribute we train a classifier which captures the specific way this attribute should be used. We show that as a result of using this model, we produce descriptions about images of people that are more natural and specific than past systems. We present an interactive, hybrid human-computer method for object classification. The method applies to classes of objects that are recognizable by people with appropriate expertise (e.g., animal species or airplane model), but not (in general) by people without such expertise. It can be seen as a visual version of the 20 questions game, where questions based on simple visual attributes are posed interactively. The goal is to identify the true class while minimizing the number of questions asked, using the visual content of the image. We introduce a general framework for incorporating almost any off-the-shelf multi-class object recognition algorithm into the visual 20 questions game, and provide methodologies to account for imperfect user responses and unreliable computer vision algorithms. We evaluate our methods on Birds-200, a difficult dataset of 200 tightly-related bird species, and on the Animals With Attributes dataset. Our results demonstrate that incorporating user input drives up recognition accuracy to levels that are good enough for practical applications, while at the same time, computer vision reduces the amount of human interaction required. Distinguishing subtle differences in attributes is valuable, yet learning to make visual comparisons remains nontrivial. Not only is the number of possible comparisons quadratic in the number of training images, but also access to images adequately spanning the space of fine-grained visual differences is limited. We propose to overcome the sparsity of supervision problem via synthetically generated images. Building on a state-of-the-art image generation engine, we sample pairs of training images exhibiting slight modifications of individual attributes. Augmenting real training image pairs with these examples, we then train attribute ranking models to predict the relative strength of an attribute in novel pairs of real images. Our results on datasets of faces and fashion images show the great promise of bootstrapping imperfect image generators to counteract sample sparsity for learning to rank.