Model security on FPGA with low latency inference
Add model security to FPGA inference. FPGAs enable developers to create hardware for new or evolving technologies and can process large networks and high-resolution images at low latency.
Accelerate AI segmentation inference in healthcare
Bring quick results to patients by using the Intel® Distribution of OpenVINO™ toolkit to run inference on medical imaging data. With the toolkit you can deploy segmentation AI models on CPU, GPU, VPU, and FPGA or even run heterogeneously for smart device execution.
Efficient and accurate binarized DNNs
Make your deep learning faster with little accuracy loss. Xnor’s efficient binarized deep learning models combined with the Intel® Distribution of OpenVINO™ toolkit can provide accurate real-time person detection at 1000 fps on an Intel® Core™ i5 processor.
Video analytics optimized for Edge AI at Scale
Edge servers can efficiently support deep learning without depending on the cloud. Using the Intel® Distribution of OpenVINO™ toolkit, you can run mixed workloads on this power- and cost-efficient inference platform.
Low power inside-out tracking for robotics, drones and more
With its small form factor and low power consumption, the Intel® RealSense™ Tracking Camera T265 has been designed to give you the tracking performance you want, off-the-shelf. Cross-platform, developer friendly simultaneous localization and mapping for all your robotics, drone and augmented reality rapid prototyping needs.
Applied depth sensing to real world applications
Intel® RealSense™ Stereo depth technology brings 3D to devices and machines that only see 2D today. Diverse capabilities and technologies make Intel® RealSense™ Depth Cameras suitable for a wide range of applications.
Large scale skeletal reconstruction for volumetric rendering
Scaling visual computing across the cloud is challenging and can be expensive. This demo shows a new framework for distributing complex media analytics workloads across many inexpensive cloud compute instances.
Road scene understanding in chaotic driving conditions
Intel’s research collaboration with the International Institute of Information Technology (IIIT), Hyderabad created the India Driving Dataset (IDD), consisting of 50,000 curated images with annotations of 34 object classes, from 182 drive sequences on Indian roads.
Probabilistic computing for human intent prediction
In this demo, we show how a combination of neural networks and approximate Bayesian computation (ABC) can achieve real-time probabilistic inference applied to a human intent prediction scenario. This interactive demo lets users explore and observe the behavior of the predictions.
Compression aware tracking for high density visual cloud analytics
We introduce an ultra-fast tracker, Compression-Aware Tracking (CAT), a method of leveraging the compressed bit-stream’s Motion Vectors (MVs) for faster object-detection. The demo also shows a popular tracking algorithm, Kernelized Correlation Filter (KCF) for comparison.
Intel® AI: In Production
Knowing where to start the development journey is important, because development and deployment of vision applications utilizing deep neural networks can be challenging selecting hardware for inference. Intel’s AI at the Edge interactive kiosk will guide you on how to find information about the Intel® Distribution of OpenVINO™ toolkit, Intel® Neural Compute Stick 2, vision accelerator kits, Intel® Vision Accelerator Design products and Intel’s partner ecosystem for AI at the Edge.
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Spotlight on community members’ work in Applied Vision Research that utilizes Intel AI Technologies.
- HadaNets: Flexible quantization strategies for neural networks
- Conference Connie: The helpful AI assistant
- Manufacturing failure detection at the edge
- Automated screening for diabetic retinopathy
- Heterogeneous computing for scientific problems
Intel Research Datasets from Intel® AI Lab
At Intel, we believe there is a virtuous cycle between datasets, research and compute that’s leading to the tremendous growth we are seeing in AI. At the Intel AI booth at CVPR, we will be demonstrating recent datasets we’ve contributed to the community, jointly with other partners, such as a multi-view satellite imagery dataset (MVOI) and a hierarchical 3D shapes dataset. Learn more.