no code implementations • 29 Aug 2022 • Yuanhan Ni, Zulin Wang, Peng Yuan, Qin Huang
This paper considers an affine frequency division multiplexing (AFDM)-based integrated sensing and communications (ISAC) system, where the AFDM waveform is used to simultaneously carry communications information and sense targets.
1 code implementation • 18 Nov 2021 • Li Yang, Mai Xu, Shengxi Li, Yichen Guo, Zulin Wang
When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i. e., a progressive quality assessment paradigm.
no code implementations • 6 Jun 2019 • Tie Liu, Mai Xu, Zulin Wang
In this paper, we establish a large-scale video database for rain removal (LasVR), which consists of 316 rain videos.
no code implementations • 15 Apr 2019 • Mai Xu, Li Yang, Xiaoming Tao, Yiping Duan, Zulin Wang
According to these findings, our SalGAIL approach applies deep reinforcement learning (DRL) to predict the head fixations of one subject, in which GAIL learns the reward of DRL, rather than the traditional human-designed reward.
1 code implementation • 26 Feb 2019 • Qunliang Xing, Zhenyu Guan, Mai Xu, Ren Yang, Tie Liu, Zulin Wang
Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video.
Ranked #5 on Video Enhancement on MFQE v2
2 code implementations • 9 Oct 2018 • Jiaxin Lu, Mai Xu, Ren Yang, Zulin Wang
In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores.
1 code implementation • ECCV 2018 • Lai Jiang, Mai Xu, Tie Liu, Minglang Qiao, Zulin Wang
Hence, an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, which is composed of the objectness and motion subnets.
no code implementations • 27 Aug 2018 • Jiaxin Lu, Mai Xu, Ren Yang, Zulin Wang
Recent studies on image memorability have shed light on the visual features that make generic images, object images or face photographs memorable.
1 code implementation • 29 Jul 2018 • Chen Li, Mai Xu, Xinzhe Du, Zulin Wang
To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences.
1 code implementation • CVPR 2018 • Ren Yang, Mai Xu, Zulin Wang, Tianyi Li
In this paper, we investigate that heavy quality fluctuation exists across compressed video frames, and thus low quality frames can be enhanced using the neighboring high quality frames, seen as Multi-Frame Quality Enhancement (MFQE).
Ranked #6 on Video Enhancement on MFQE v2
1 code implementation • 30 Oct 2017 • Yuhang Song, Mai Xu, Jianyi Wang, Minglang Qiao, Liangyu Huo, Zulin Wang
Finally, the experiments validate that our approach is effective in both offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.
no code implementations • 20 Sep 2017 • Ren Yang, Mai Xu, Tie Liu, Zulin Wang, Zhenyu Guan
Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P frames of HEVC videos.
Multimedia
1 code implementation • 19 Sep 2017 • Lai Jiang, Mai Xu, Zulin Wang
We further find from our database that there exists a temporal correlation of human attention with a smooth saliency transition across video frames.
1 code implementation • 19 Sep 2017 • Mai Xu, Tianyi Li, Zulin Wang, Xin Deng, Ren Yang, Zhenyu Guan
Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra- and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network.
no code implementations • ICCV 2015 • Mai Xu, Yun Ren, Zulin Wang
For modeling attention on faces and facial features, the proposed method learns the Gaussian mixture model (GMM) distribution from the fixations of eye tracking data as the top-down features for saliency detection of face images.