TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Since its appearance, Generative Adversarial Networks (GANs) [2] have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the results so far didn't look adequate for the quality standard of visual media production industry. We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images. All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects.

SOLUTION: Megapixel Size Image Creation using Generative Adversarial Networks

PROBLEM: How to build a machine learning method that can continuously gain structured visual knowledge by learning structured facts? Our goal in this paper is to address this question by proposing a problem setting, where training data comes as structured facts in images with different types including (1) objects(e.g., <boy>), (2) attributes (e.g., <boy,tall>), (3) actions (e.g., <boy, playing>), (4) interactions (e.g., <boy, riding, a horse >). Each structured fact has a semantic language view (e.g., < boy, playing>) and a visual view (an image with this fact). A human is able to efficiently gain visual knowledge by learning facts in a never ending process, and as we believe in a structured way (e.g., understanding "playing" is the action part of < boy, playing>, and hence can generalize to recognize <girl, playing > if just learn <girl> additionally). Inspired by human visual perception, we propose a model that is (1) able to learn a representation, we name as wild-card, which covers different types of structured facts, (2) could flexibly get fed with structured fact language-visual view pairs in a never ending way to gain more structured knowledge, (3) could generalize to unseen facts, and (4) allows retrieval of both the fact language view given the visual view (i.e., image) and vice versa. We also propose a novel method to generate hundreds of thousands of structured fact pairs from image caption data, which are necessary to train our model and can be useful for other applications.

SOLUTION: SHERLOCK: MODELING STRUCTURED KNOWLEDGE IN IMAGES

PROBLEM: Seven years on from OWL becoming a W3C recommendation, and two years on from the more recent OWL 2 W3C recommendation, OWL has still experienced only patchy uptake on the Web. Although certain OWL features (like owl:sameAs) are very popular, other features of OWL are largely neglected by publishers in the Linked Data world. This may suggest that despite the promise of easy implementations and the proposal of tractable profiles suggested in OWL's second version, there is still no "right" standard fragment for the Linked Data community. In this paper, we (1) analyse uptake of OWL on the Web of Data, (2) gain insights into the OWL fragment that is actually used/usable on the Web, where we arrive at the conclusion that this fragment is likely to be a simplified profile based on OWL RL, (3) propose and discuss such a new fragment, which we call OWL LD (for Linked Data).

SOLUTION:
OWL: Yet to arrive on the Web of Data?