Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence. Modeling sequences of evolving images may provide better representations and models of motion and may ultimately be used for forecasting, simulation, or video generation. Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in complex patterns and one needs to infer the underlying pattern sequence and generate the next image in the sequence. For this, we develop a novel Contextual Generative Adversarial Network based on Recurrent Neural Networks (Context-RNN-GANs), where both the generator and the discriminator modules are based on contextual history (modeled as RNNs) and the adversarial discriminator guides the generator to produce realistic images for the particular time step in the image sequence. We evaluate the Context-RNN-GAN model (and its variants) on a novel dataset of Diagrammatic Abstract Reasoning, where it performs competitively with 10th-grade human performance but there is still scope for interesting improvements as compared to college-grade human performance. We also evaluate our model on a standard video next-frame prediction task, achieving improved performance over comparable state-of-the-art.
@cite_31 introduced IQ Tests to measure the success of an individual at adapting to a specific situation under a specific condition. Visual problems in intelligence tests have been among the earliest and continuously researched problems in AI. It has been looked at in terms of propositional logic beginning with @cite_7 and more recently by @cite_30 . Recently, there has also been significant interest in building systems that compete with humans on a variety of tasks such as geometry-based problems @cite_5 , physics-based problems @cite_22 , repetition and symmetry detection @cite_22 , visual question answering @cite_40 , and verbal reasoning and analogy @cite_36 @cite_23 .
@cite_30 @cite_22 @cite_7 @cite_36 @cite_40 @cite_23 @cite_5 @cite_31
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Intelligence Quotient (IQ) Test is a set of standardized questions designed to evaluate human intelligence. Verbal comprehension questions appear very frequently in IQ tests, which measure human's verbal ability including the understanding of the words with multiple senses, the synonyms and antonyms, and the analogies among words. In this work, we explore whether such tests can be solved automatically by artificial intelligence technologies, especially the deep learning technologies that are recently developed and successfully applied in a number of fields. However, we found that the task was quite challenging, and simply applying existing technologies (e.g., word embedding) could not achieve a good performance, mainly due to the multiple senses of words and the complex relations among words. To tackle these challenges, we propose a novel framework consisting of three components. First, we build a classifier to recognize the specific type of a verbal question (e.g., analogy, classification, synonym, or antonym). Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries. Third, for each type of questions, we propose a specific solver based on the obtained distributed word representations and relation representations. Experimental results have shown that the proposed framework can not only outperform existing methods for solving verbal comprehension questions but also exceed the average performance of the Amazon Mechanical Turk workers involved in the study. The results indicate that with appropriate uses of the deep learning technologies we might be a further step closer to the human intelligence. The goal of the current research was to introduce a new component of intelligence: visual-object intelligence, that reflects one's ability to process information about visual appearances of objects and their pictorial properties (e.g., shape, color and texture) as well as to demonstrate that it is distinct from visual-spatial intelligence, which reflects one's ability to process information about spatial relations and manipulate objects in space. Study 1 investigated the relationship between performance on various measures of visual-object and visual-spatial abilities, and areas of specialization (visual art, science and humanities). Study 2 examined qualitative differences in approaches to interpreting visual abstract information between visual artists, scientists and humanities social science professionals. Study 3 investigated qualitative differences in visual-object versus visual-spatial processing by examining how members of different professions generate, transform, inspect, and manipulate visual images. The results of the three studies demonstrated that visual-object ability satisfies the requirements of an independent component of intelligence: (1) it uniquely relates to specialization in visual art; (2) it supports processing of abstract visual-object information; and (3) it has unique quantitative and qualitative characteristics, distinct from those of visual-spatial processing. In the field of objective image quality assessment (IQA), the Spearman's @math and Kendall's @math are two most popular rank correlation indicators, which straightforwardly assign uniform weight to all quality levels and assume each pair of images are sortable. They are successful for measuring the average accuracy of an IQA metric in ranking multiple processed images. However, two important perceptual properties are ignored by them as well. Firstly, the sorting accuracy (SA) of high quality images are usually more important than the poor quality ones in many real world applications, where only the top-ranked images would be pushed to the users. Secondly, due to the subjective uncertainty in making judgement, two perceptually similar images are usually hardly sortable, whose ranks do not contribute to the evaluation of an IQA metric. To more accurately compare different IQA algorithms, we explore a perceptually weighted rank correlation indicator in this paper, which rewards the capability of correctly ranking high quality images, and suppresses the attention towards insensitive rank mistakes. More specifically, we focus on activating valid' pairwise comparison towards image quality, whose difference exceeds a given sensory threshold (ST). Meanwhile, each image pair is assigned an unique weight, which is determined by both the quality level and rank deviation. By modifying the perception threshold, we can illustrate the sorting accuracy with a more sophisticated SA-ST curve, rather than a single rank correlation coefficient. The proposed indicator offers a new insight for interpreting visual perception behaviors. Furthermore, the applicability of our indicator is validated in recommending robust IQA metrics for both the degraded and enhanced image data. Achieving artificial visual reasoning — the ability to answer image-related questions which require a multi-step, high-level process — is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4 error rate. We outperform the next best end-to-end method (4.5 ) and even methods that use extra supervision (3.1 ). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively. Index Terms: Deep Learning, Language and Vision Note: A full paper extending this study is available at http: arxiv.org abs 1709.07871, with additional references , experiments, and analysis.