Drop by our booth (1404) at #CVPR to learn about our AI software and generative AI video model for autonomous driving. We're actively hiring for multiple roles in AI/ML research and engineering!
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From self-driving cars to healthcare advancements, the possibilities are endless! I've been astounded by the rapid advancements in deep learning technology shaping various industries. We should expect to witness the transformative impact of deep learning algorithms, revolutionizing fields like autonomous vehicles and personalized healthcare with unprecedented precision and efficiency. #DeepLearningInnovation #TechAdvancements #FutureTech #AIRevolution #EndlessPossibilities #uk #qwertyexperts
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Helm.ai Launches VidGen-1: AI that Creates Realistic Driving Videos This innovative technology marks a significant leap forward in the development and validation of autonomous driving systems. Vladislav Voroninski, CEO and Co-Founder of Helm.ai, highlighted the significance of this breakthrough, “We’ve made a technical breakthrough in generative AI for video to develop VidGen-1, setting a new bar in the autonomous driving domain. Combining our Deep Teaching technology, which we’ve been developing for years, with additional in-house innovation on generative DNN architectures results in a highly effective and scalable method for producing realistic AI-generated videos.” Read more: https://lnkd.in/egqXvfpG
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📃Scientific paper: SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving Abstract: To mitigate the challenges arising from partial occlusion in human pose keypoint based pedestrian detection methods , we present a novel pedestrian pose keypoint completion method called the separation and dimensionality reduction-based generative adversarial imputation networks (SDR-GAIN) . Firstly, we utilize OpenPose to estimate pedestrian poses in images. Then, we isolate the head and torso keypoints of pedestrians with incomplete keypoints due to occlusion or other factors and perform dimensionality reduction to enhance features and further unify feature distribution. Finally, we introduce two generative models based on the generative adversarial networks (GAN) framework, which incorporate Huber loss, residual structure, and L1 regularization to generate missing parts of the incomplete head and torso pose keypoints of partially occluded pedestrians, resulting in pose completion. Our experiments on MS COCO and JAAD datasets demonstrate that SDR-GAIN outperforms basic GAIN framework, interpolation methods PCHIP and MAkima, machine learning methods k-NN and MissForest in terms of pose completion task. Furthermore, the SDR-GAIN algorithm exhibits a remarkably short running time of approximately 0.4ms and boasts exceptional real-time performance. As such, it holds significant practical value in the domain of autonomous driving, wherein high system response speeds are of paramount importance. Specifically, it excels at rapidly and precisely capturing human pose key po... Discover the rest of the scientific article on es/iode ➡️https://etcse.fr/QSc
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Digital Transformation Lead, AI Consulting, Business Development, Healthcare IT, Project & Programme Management, AWS
Prof. Liu Yong and team introduced a novel framework, CKT-RCM, to tackle long-tail distribution issues in computer vision. Their work on Panoptic Scene Graph (PSG) aims to enhance scene understanding and support tasks like scene description. By leveraging prior knowledge and contextual information, they improved relationship inference during PSG processes. This study emphasizes the importance of prior knowledge and contextual information for accurate predictions. The CKT-RCM framework, based on CLIP, integrates a cross-attention mechanism for relational context extraction. Overall, this research enhances scene perception for robots and autonomous vehicles. #psg #ComputerVision #sceneperception #innovativeframework
Researchers improve scene perception with innovative framework
techxplore.com
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📖 NEW JOURNAL PAPER PUBLICATION 📖 Our paper titled "Enhancing end-to-end control in autonomous driving through kinematic-infused and visual memory imitation learning" has been accepted for publication in the Neurocomputing journal. Joint work by Sergio Paniego Blanco, Roberto Calvo, and Jose María Cañas. 💡 In this paper, we explore, study, and compare various alternatives to enhance the capabilities of an end-to-end control system for autonomous driving based on imitation learning by adding visual memory and kinematic input data to the deep learning architectures that govern the vehicle. Online experimental validation was conducted using the CARLA simulator. 💻 Website with open-source resources and videos: https://lnkd.in/dWJcjz5u 📖 Paper: https://lnkd.in/dfB9XjqS
Enhancing End-to-End Control in Autonomous Driving through Kinematic-Infused and Visual Memory Imitation Learning
roboticslaburjc.github.io
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Top 10 AI Jobs for Data Scientists to Apply for in 2024 https://lnkd.in/gUHUF7TW Dive into the future of AI! Explore the Top 10 AI Jobs that data scientists should be eyeing in 2024. From shaping the architecture of tomorrow's systems to driving innovation in autonomous vehicles and revolutionizing healthcare, the opportunities are limitless! Stay ahead in the dynamic world of AI. #AIJobs #FutureOfWork #DataScientists #MachineLearning #AIInnovation #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine
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I am looking to speak with people well versed in and working directly or indirectly with data labelling and annotation, autonomous driving, image classification, semantic segmentation and related terminologies. Key question to answer here: how much is it costing you to label your datasets and what do you need to expedite the process? Do reach out if you know anyone! #machinelearning #datalabelling #semanticsearch #autonomousdriving
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Who knew? Thanks to Azadeh Dinparastdjadid for sharing her thoughts on how it is actually possible to model surprising, completely unexpected road traffic events - by using a a generative model capable of producing beliefs that reflect an agent’s expectations - and then violating them! It's a fascinating research area that ADAS & Autonomous Vehicle International readers can discover more about at the forthcoming #AVTExpoCA conference!
Discover how, for the first time, computational models of surprise rooted in cognitive science and neuroscience, when combined with state-of-the-art ML generative models, can detect surprising human behavior in complex, dynamic environments, including road traffic. Azadeh Dinparastdjadid, a senior research scientist on the safety research and best practices team at Waymo, reveals all ahead of her presentation on the same theme at the ADAS & AUTONOMOUS VEHICLE TECHNOLOGY EXPO, California , which takes place September 20 & 21, 2023, in Santa Clara. #AVTExpoCA #robotaxis #automateddriving #autonomousvehicles https://lnkd.in/e7dd_A86
FEATURE: Waymo – how to measure surprising road user behavior
https://www.autonomousvehicleinternational.com
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Langchain Developer build Ai powered applications
1moCan I come in as an intern?