Zero-shot Recognition (ZSR) is to learn recognition models for novel classes without labeled data. It is a challenging task and has drawn considerable attention in recent years. The basic idea is to transfer knowledge from seen classes via the shared attributes. This paper focus on the transductive ZSR, i.e., we have unlabeled data for novel classes. Instead of learning models for seen and novel classes separately as in existing works, we put forward a novel joint learning approach which learns the shared model space (SMS) for models such that the knowledge can be effectively transferred between classes using the attributes. An effective algorithm is proposed for optimization. We conduct comprehensive experiments on three benchmark datasets for ZSR. The results demonstrates that the proposed SMS can significantly outperform the state-of-the-art related approaches which validates its efficacy for the ZSR task.

Viewpoint estimation, especially in case of multiple object classes, remains an important and challenging problem. First, objects under different views undergo extreme appearance variations, often making within-class variance larger than between-class variance. Second, obtaining precise ground truth for real-world images, necessary for training supervised viewpoint estimation models, is extremely difficult and time consuming. As a result, annotated data is often available only for a limited number of classes. Hence it is desirable to share viewpoint information across classes. Additional complexity arises from unaligned pose labels between classes, i.e. a side view of a car might look more like a frontal view of a toaster, than its side view. To address these problems, we propose a metric learning approach for joint class prediction and pose estimation. Our approach allows to circumvent the problem of viewpoint alignment across multiple classes, and does not require dense viewpoint labels. Moreover, we show, that the learned metric generalizes to new classes, for which the pose labels are not available, and therefore makes it possible to use only partially annotated training sets, relying on the intrinsic similarities in the viewpoint manifolds. We evaluate our approach on two challenging multi-class datasets, 3DObjects and PASCAL3D+.

We propose a new zero-shot Event-Detection method by Multi-modal Distributional Semantic embedding of videos. Our model embeds object and action concepts as well as other available modalities from videos into a distributional semantic space. To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) semantic embedding of concepts definitions, and (c) retrieve videos by free text event query (e.g., "changing a vehicle tire") based on their content. We first embed the video into the multi-modal semantic space and then measure the similarity between videos with the event query in free text form. We validated our method on the large TRECVID MED (Multimedia Event Detection) challenge. Using only the event title as a query, our method outperformed the state-the-art that uses big descriptions from 12.6\% to 13.5\% with MAP metric and from 0.73 to 0.83 with ROC-AUC metric. It is also an order of magnitude faster.

Microsoft is the world's most valuable company, with a 261 billion market cap. And an IBM computer named Deep Blue defeats Garry Kasparov, reigning world chess champion and, at the time, the highest-ranked chess player to have ever lived.

This talk was held on Wed, Feb 24 2016 Dr. Demis Hassabis is the Co-Founder and CEO of DeepMind, the world's leading General Artificial Intelligence (AI) company, which was acquired by Google in 2014 in their largest ever European acquisition. Demis draws on his eclectic experiences as an AI researcher, neuroscientist and videogames designer to discuss what is happening at the cutting edge of AI research, its future impact on fields such as science and healthcare, and how developing AI may help us better understand the human mind.

Demis Hassabis has a modest demeanour and an unassuming countenance, but he is deadly serious when he tells me he is on a mission to "solve intelligence, and then use that to solve everything else". Coming from almost anyone else, the statement would be laughable; from him, not so much. Hassabis is the 39-year-old former chess master and video-games designer whose artificial intelligence research start-up, DeepMind, was bought by Google in 2014 for a reported 625 million. He is the son of immigrants, attended a state comprehensive in Finchley and holds degrees from Cambridge and UCL in computer science and cognitive neuroscience. A "visionary" manager, according to those who work with him, Hassabis also reckons he has found a way to "make science research efficient" and says he is leading an "Apollo programme for the 21st century". He's the sort of normal-looking bloke you wouldn't look twice at on the street, but Tim Berners-Lee once described him to me as one of the smartest human beings on the planet. Artificial intelligence is already all around us, of course, every time we interrogate Siri or get a recommendation on Android. And in the short term, Google products will surely benefit from Hassabis's research, even if improvements in personalisation, search, YouTube, and speech and facial recognition are not presented as "AI" as such. "It's just stuff that works.") In the longer term, though, the technology he is developing is about more than emotional robots and smarter phones.

For artificial intelligence, 2016 has been called "like 2015 on steroids." Want to learn more about what that really means? Follow these 10 twitter users for an insider's take on the latest developments in AI. The brains behind Google's AI platform DeepMind, Hassabis is arguably one of the most important voices in the AI world today. AlphaGo, created by DeepMind, has surpassed expectations, winning in the game of Go ten years before experts predicted.

Microsoft is the world's most valuable company, with a 261 billion market cap. And an IBM computer named Deep Blue defeats Garry Kasparov, reigning world chess champion and, at the time, the highest-ranked chess player to have ever lived. Though it was not the first time man has lost to machine, it is perhaps the most prominent, highly publicized by IBM and widely covered by the global media. It was viewed as a milestone for AI, the true arrival of computer intelligence. The world celebrated the achievement of technology -- or offered doomsday predictions of a robot revolution.

Elon Musk has not been shy about his concerns over artificial intelligence turning evil. So it wasn't a surprise in December when Musk announced the formation of OpenAI, an open-source, non-profit focused on advancing "digital intelligence in the way that is most likely to benefit humanity as a whole." That's all well and good, but not much has been revealed about what exactly OpenAI is working on. OpenAI's co-founder and CTO Greg Brockman told Tech Insider that OpenAI is primarily focusing on advancing machine learning, which is the technology that enables computers to learn how to complete tasks through experience. Specifically, the company is focusing on two key types of machine learning that every major tech company is investing in right now.

Elon Musk has not been shy about his concerns over artificial intelligence turning evil. So it wasn't a surprise in December when Musk announced the formation of OpenAI, an open-source, non-profit focused on advancing "digital intelligence in the way that is most likely to benefit humanity as a whole." That's all well and good, but not much has been revealed about what exactly OpenAI is working on. OpenAI's co-founder and CTO told Tech Insider that OpenAI is primarily focusing on advancing machine learning, which is the technology that enables computers to learn how to complete tasks through experience. Specifically, the company is focusing on two key types of machine learning that every major tech company is investing in right now.