Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images. We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input. First, we use the text description as initial input and conduct dependency parsing to extract the syntactic structure and analyse the semantic aspect, including object quantities, to extract the scene graph. Then, we train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks, and it generates text representation which integrates textual and visual semantic information. The text representation is aggregated with word-level and sentence-level embedding to generate both visual contextual word and sentence representation. For the evaluation, we attached VICTR to the state-of-the-art models in text-to-image generation.VICTR is easily added to existing models and improves across both quantitative and qualitative aspects.

Although 3D Convolutional Neural Networks (CNNs) are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and computational requirements. Compressing such networks by pruning therefore becomes highly desirable. However, pruning 3D CNNs is largely unexplored possibly because of the complex nature of typical pruning algorithms that embeds pruning into an iterative optimization paradigm. In this work, we introduce a Resource Aware Neuron Pruning (RANP) algorithm that prunes 3D CNNs at initialization to high sparsity levels. Specifically, the core idea is to obtain an importance score for each neuron based on their sensitivity to the loss function. This neuron importance is then reweighted according to the neuron resource consumption related to FLOPs or memory. We demonstrate the effectiveness of our pruning method on 3D semantic segmentation with widely used 3D-UNets on ShapeNet and BraTS'18 as well as on video classification with MobileNetV2 and I3D on UCF101 dataset. In these experiments, our RANP leads to roughly 50-95 reduction in FLOPs and 35-80 reduction in memory with negligible loss in accuracy compared to the unpruned networks. This significantly reduces the computational resources required to train 3D CNNs. The pruned network obtained by our algorithm can also be easily scaled up and transferred to another dataset for training.

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Perched in his fire tower high above the pine trees, Nick Dutton leans back and nods to the cascading hills and mountains behind him. "I love being out here, just away from stuff," he says. "I mean, you can't really complain." Dutton, a fire tower operator, is sitting in his office, a tiny cabin propped high above the treetops by metal supports that sway with the wind. His walls are littered with compass points and references, each a guide to the bush stretching in every direction along the eastern ACT-NSW border.

Amazon Go is the first store where no checkout is required. Customer simply enter the store using the Amazon Go app to browse and take the required products or items they want and then leave. Customer being able to purchase, products without suing a counter or checkout. The following video shows how Self-driving Robot (Delivery Bot and named as YAPE) brings goods directly to you, it uses Facial Recognition to recognize the customer to deliver. It makes delivery fast and easy, bot easily navigates sidewalks. YAPE has a 70 kg loading capacity and can travel 80km on a single charge.

Artificial intelligence (AI) is already delivering on making aspects of health care more efficient. Over time it will likely be essential to supporting clinical and other applications that result in more insightful and effective care and operations. AI has multiple use cases throughout health plan, pharmacy benefit manager (PBM), and health system enterprises today, and with more interoperable and secure data, it is likely to be a critical engine behind analytics, insights, and the decision-making process. Enterprises that lean into adoption are likely to gain immediate returns through cost reduction and gain competitive advantage over the longer term as they use AI to transform their products and services to better engage with consumers. Get the Deloitte Insights app.

Technology evolution is no longer keeping pace with the growth of data. We are facing problems storing and processing the huge amounts of data produced every day. People rely on data-intensive applications and new paradigms (for example, edge computing) to try to keep computation closer to where data is produced and needed. Thus, the need to store and query data in devices where capacity is surpassed by data volume is routine today, ranging from astronomy data to be processed by supercomputers, to personal data to be processed by wearable sensors. The scale is different, yet the underlying problem is the same.

How one gets educated for AI continues to be an area worth exploring with many options available. Charting one's career as a member of a newly-formed team working to leverage AI to help the business is best met with creativity and patience. It's as much a mission to find out how organizations are setting up for AI development as it is about finding out what you really want to do. The experience of one now-veteran machine modeler could be timely guidance for many in this context. Daniel Bourke is an entrepreneur running a YouTube site and writing about technology.

Artificial intelligence (AI) is no longer the stuff of science fiction. The technology is already disrupting multiple industries, many of which impact you on a daily basis. Own an iPhone X? Its facial recognition system is powered by AI. Ever been redirected by Google Maps because of an accident or construction ahead? And those are just a couple of small examples.

Automatic fall detection is a vital technology for ensuring the health and safety of people. Home-based camera systems for fall detection often put people's privacy at risk. Thermal cameras can partially or fully obfuscate facial features, thus preserving the privacy of a person. Another challenge is the less occurrence of falls in comparison to the normal activities of daily living. As fall occurs rarely, it is non-trivial to learn algorithms due to class imbalance. To handle these problems, we formulate fall detection as an anomaly detection within an adversarial framework using thermal imaging. We present a novel adversarial network that comprises of two-channel 3D convolutional autoencoders which reconstructs the thermal data and the optical flow input sequences respectively. We introduce a technique to track the region of interest, a region-based difference constraint, and a joint discriminator to compute the reconstruction error. A larger reconstruction error indicates the occurrence of a fall. The experiments on a publicly available thermal fall dataset show the superior results obtained compared to the standard baseline.