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Deqing Sun

Deqing Sun

I am a staff research scientist at Google working on computer vision and machine learning. I received my Ph.D. in Computer Science from Brown University, M.Phil. in Electronic Engineering from the Chinese University of Hong Kong, and B.Eng. in Electronic and Information Engineering from Harbin Institute of Technology. I was a postdoctoral fellow at Harvard University and then a senior research scientist at NVIDIA before joining Google. I served as an area chair for CVPR/ECCV/BMVC, and co-organized several workshops/tutorials at CVPR/ECCV/SIGGRAPH. I am a recipient of the PAMI Young Researcher award in 2020, the Longuet-Higgins prize at CVPR 2020, the best paper honorable mention award at CVPR 2018, and the first prize in the robust optical flow competition at CVPR 2018 and ECCV 2020.
Authored Publications
Google Publications
Other Publications
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    Kubric: A scalable dataset generator
    Anissa Yuenming Mak
    Austin Stone
    Carl Doersch
    Cengiz Oztireli
    Charles Herrmann
    Daniel Rebain
    Derek Nowrouzezahrai
    Dmitry Lagun
    Fangcheng Zhong
    Florian Golemo
    Francois Belletti
    Henning Meyer
    Hsueh-Ti (Derek) Liu
    Issam Laradji
    Klaus Greff
    Kwang Moo Yi
    Matan Sela
    Noha Radwan
    Thomas Kipf
    Tianhao Wu
    Vincent Sitzmann
    Yilun Du
    Yishu Miao
    Preview abstract Data is the driving force of machine learning. The amount and quality of training data is often more important for the performance of a system than the details of its architecture. Data is also an important tool for testing specific hypothesis, and for empirically evaluating the behaviour of complex systems. Synthetic data generation represents a powerful tool that can address all these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can circumvent privacy and legal concerns. Unfortunately the toolchain for generating data is less well developed than that for building models. We aim to improve this situation by introducing Kubric: a scalable open-source pipeline for generating realistic image and video data with rich ground truth annotations. We also publish a collection of generated datasets and baseline results on several vision tasks. View details
    FILM: Frame Interpolation for Large Motion
    Fitsum Reda
    Eric Tabellion
    Proceedings of the European conference on computer vision (ECCV) (2022)
    Preview abstract We present a frame interpolation algorithm that synthesizes an engaging slow-motion video from near-duplicate photos which often exhibit large scene motion. Near-duplicates interpolation is an interesting new application, but large motion poses challenges to existing methods. To address this issue, we adapt a feature extractor that shares weights across the scales, and present a “scale-agnostic” motion estimator. It relies on the intuition that large motion at finer scales should be similar to small motion at coarser scales, which boosts the number of available pixels for large motion supervision. To inpaint wide disocclusions caused by large motion and synthesize crisp frames, we propose to optimize our network with the Gram matrix loss that measures the correlation difference between features. To simplify the training process, we further propose a unified single-network approach that removes the reliance on additional optical-flow or depth network and is trainable from frame triplets alone. Our approach outperforms state-of-the-art methods on the Xiph large motion benchmark while performing favorably on Vimeo90K, Middlebury and UCF101. Source codes and pre-trained models are available at https://film-net.github.io. View details
    AutoFlow: Learning a Better Training Set for Optical Flow
    Daniel Vlasic
    Charles Herrmann
    Varun Jampani
    Huiwen Chang
    Ramin Zabih
    Ce Liu
    Preview abstract Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the process, we present AutoFlow, a simple and effective method to render training data for optical flow that optimizes the performance of a model on a target dataset. AutoFlow takes a layered approach to render synthetic data, where the motion, shape, and appearance of each layer are controlled by learnable hyperparameters. Experimental results show that AutoFlow achieves state-of-the-art accuracy in pre-training both PWC-Net and RAFT. Our code and data are available at https://autoflow-google.github.io. View details
    HumanGPS: Geodesic PreServing Feature for Dense Human Correspondence
    Danhang "Danny" Tang
    Mingsong Dou
    Kaiwen Guo
    Cem Keskin
    Sofien Bouaziz
    Ping Tan
    Computer Vision and Pattern Recognition 2021 (2021), pp. 8
    Preview abstract In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses. Prior art either assumes small motion between frames or relies on local descriptors, which cannot handle large motion or visually ambiguous body parts, e.g. left v.s. right hand. In contrast, we propose a deep learning framework that maps each pixel to a feature space, where the feature distances reflect the geodesic distances among pixels as if they were projected onto the surface of a 3D human scan. To this end, we introduce novel loss functions to push features apart according to their geodesic distances on the surface. Without any semantic annotation, the proposed embeddings automatically learn to differentiate visually similar parts and align different subjects into an unified feature space. Extensive experiments show that the learned embeddings can produce accurate correspondences between images with remarkable generalization capabilities on both intra and inter subjects. View details
    Learnable Cost Volume Using the Cayley Representation
    Taihong Xiao
    Jinwei Yuan
    Xin-Yu Zhang
    Kehan Xu
    The European Conference on Computer Vision (ECCV) (2020)
    Preview abstract Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors. However, the standard inner product in the commonly-used cost volume may limit the representation capacity of flow models because it neglects the correlation among different channel dimensions and weighs each dimension equally. To address this issue, we propose a learnable cost volume (LCV) using an elliptical inner product, which generalizes the standard inner product by a positive definite kernel matrix. To guarantee its positive definiteness, we perform spectral decomposition on the kernel matrix and re-parameterize it via the Cayley representation. The proposed LCV is a lightweight module and can be easily plugged into existing models to replace the vanilla cost volume. Experimental results show that the LCV module not only improves the accuracy of state-of-the-art models on standard benchmarks, but also promotes their robustness against illumination change, noises, and adversarial perturbations of the input signals. View details
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