News

  • [2021.07.15 Update] For Postgraduate Admissions,please click Postgraduate Admission
  • [2021.07.15 Update] For Undergraduate Internship,please click Undergraduate Internship
  • [2021.02.13 Update] For Post-Doc Positions,please click Post-Doc Recruitment
  • 2021.07.29:J. Yi's paper on recommendation system was accepted to IEEE TMM.
  • 2021.07.19:X. Cheng's paper on video frame interpolation was accepted to IEEE TPAMI.
  • 2021.06.11:N. Li's paper on blackbox attack was accepted to IEEE TIP.
  • 2021.05.11:Our team won 3rd place in CVPR2021 AICity Track 4.
  • 2021.03.19:F. Liu's paper on video coding was accepted to IEEE TIP.
  • 2021.01.11:Joint work with Bell Labs et al. has been published as ITU-T P.919.
  • 2020.09.09:Prof. Chen has become Associate Editor of IEEE TIP.

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Recent Achievements

  • 20207.22
    In this work, we propose a novel black-box attack approach that can directly minimize the induced distortion by learning the noise distribution of the adversarial example, assuming only loss-oracle access to the black-box network. Our attack results in low distortion as validated on ImageNet. Read more...
  • 20206.15
    In this paper, we propose an enhanced deformable separable convolution (EDSC) based network to obtain information from non-local neighborhood than is capable to produce multiple in-between frames. Experimental results show that our method performs favorably against the state-of-the-art methods. Read more...
  • 20205.27
    In this work, a novel 6DoF mesh saliency database is developed. We also propose a 6DoF mesh saliency detection algorithm together with an evaluation metric for 6DoF experiments accordingly. Moreover, some state-of-the-art saliency detection methods have been extended as benchmarks. Read more...
  • 20203.28
    In this paper, we propose a multimodal variational encoder-decoder (MMVED) framework for micro-video popularity prediction tasks. MMVED learns a stochastic Gaussian embedding of a micro-video that is informative to its popularity level while preserves the inherent uncertainties simultaneously. Read more...
  • 20203.24
    In this paper, we propose a refined Adversarial Inverse Reinforcement Learning (rAIRL) method to handle the reward ambiguity problem by disentangling reward for each word in a sentence, as well as achieve stable adversarial training by refining the loss function to shift the generator towards Nash equilibrium. Read more...