Yurong Chen
Senior Research Director & Principle Research Scientist, Cognitive Computing Lab, Intel Labs China
Dr. Yurong Chen is a Principle Research Scientist and Sr. Research Director at Intel Corporation, and Director of Cognitive Computing Lab at Intel Labs China. Currently, he’s responsible for driving cutting-edge Visual Cognition and Machine Learning research for Intel smart computing. He is also the co-owner of Intel Labs “Visual Understanding and Synthesis” program, driving research innovation in smart visual data processing technologies on Intel platforms across Intel Labs. He drove the research and development of Deep Learning (DL) based Visual Understanding (VU) and leading Face Analysis technologies to impact Intel architectures/platforms and delivered core technologies to help differentiate Intel products including Intel RealSense SDK, CV SDK, IOT video E2E analytics solutions and client apps. He led the team to win Intel China Award (Top team award of Intel China) 2016, Intel Labs Academic Awards (Top award of Intel labs) – Gordy Award 2016, 2015 and 2014 for outstanding research achievements on DL based VU, Multimodal Emotion Recognition and Advanced Visual Analytics. Dr. Chen joined Intel in 2004 after finishing his postdoctoral research in the Institute of Software, CAS. He received his Ph.D. degree from Tsinghua University in 2002. He has published over 50 technical papers, and holds 10+ issued/pending US/PCT patents and 30+ patent applications.
Expertise:
- AI and Society
- Computer Vision
- Parallel Computing
- Reinforcement Learning (RL)
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Sparse DNNs with Improved Adversarial Robustness
Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting…
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Efficient Semantic Scene Completion Network with Spatial Group…
We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks. SGC is orthogonal to group…
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Explicit Loss-Error-Aware Quantization for Deep Neural Networks
Benefiting from tens of millions of hierarchically stacked learnable parameters, Deep Neural Networks (DNNs) have demonstrated overwhelming accuracy on a…
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Learning Visual Knowledge Memory Networks for Visual Question…
Visual question answering (VQA) requires joint comprehension of images and natural language questions, where many questions can't be directly or…
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Analyzing and Understanding Visual Data
Currently, more than 75% of all internet traffic is visual (video/images). Total traffic is exploding, projected to jump from 1.2…
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Learning Supervised Scoring Ensemble for Emotion Recognition in…
State-of-the-art approaches for the previous emotion recognition in the wild challenges are usually built on prevailing Convolutional Neural Networks (CNNs).…
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Decoder Network over Lightweight Reconstructed Feature for Fast…
Recently, the community of style transfer is trying to incorporate semantic information into traditional system. This practice achieves better perceptual…
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Learning Accurate Low-Bit Deep Neural Networks with Stochastic…
Low-bit deep neural networks (DNNs) become critical for embedded applications due to their low storage requirement and computing efficiency. However,…
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DSOD: Learning Deeply Supervised Object Detectors from Scratch
We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch. State-of-the-art object objectors rely…
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RON: Reverse Connection with Objectness Prior Networks for…
We present RON, an efficient and effective framework for generic object detection. Our motivation is to smartly associate the best…
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Physics Inspired Optimization on Semantic Transfer Features: An…
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the…
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Network Sketching: Exploiting Binary Structure in Deep CNNs
Convolutional neural networks (CNNs) with deep architectures have substantially advanced the state-of-the-art in computer vision tasks. However, deep networks are…
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Weakly Supervised Dense Video Captioning
This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video…
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Incremental Network Quantization: Towards Lossless CNNS with Low-Precision…
This paper presents incremental network quantization (INQ), a novel method, targeting to efficiently convert any pre-trained full-precision convolutional neural network…
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HoloNet: Towards Robust Emotion Recognition in the Wild
In this paper, we present HoloNet, a well-designed Convolutional Neural Network (CNN) architecture regarding our submissions to the video based…
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Optimal Bayesian Hashing for Efficient Face Recognition
In practical applications, it is often observed that high-dimensional features can yield good performance, while being more costly in both…
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Regional Gating Neural Networks for Multi-label Image Classification
This paper proposes a novel deep learning framework for multi-label image classification, namely regional gating neural networks (RGNN). The motivation…
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Dynamic Network Surgery for Efficient DNNs
Deep learning has become a ubiquitous technology to improve machine intelligence. However, most of the existing deep models are structurally…
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HyperNet: Towards Accurate Region Proposal Generation and Joint…
Almost all of the current top-performing object detection networks employ region proposals to guide the search for object instances. State-of-the-art…
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Capturing AU-Aware Facial Features and Their Latent Relations…
The Emotion Recognition in the Wild (EmotiW) Challenge has been held for three years. Previous winner teams primarily focus on…