no code implementations • 10 Oct 2022 • Fan Zhang, ShaoDi You, Yu Li, Ying Fu
In this paper, we propose GTAV-NightRain dataset, which is a large-scale synthetic night-time rain streak removal dataset.
no code implementations • Computer Vision and Image Understanding 2022 • YaHui Zhang, ShaoDi You, Sezer Karaoglu, Theo Gevers
Multi-person 3D pose estimation with absolute depths for a fisheye camera is a challenging task but with valuable applications in daily life, especially for video surveillance.
2 code implementations • ICCV 2021 • Ziteng Cui, Guo-Jun Qi, Lin Gu, ShaoDi You, Zenghui Zhang, Tatsuya Harada
To enhance object detection in a dark environment, we propose a novel multitask auto encoding transformation (MAET) model which is able to explore the intrinsic pattern behind illumination translation.
Ranked #1 on 2D object detection on ExDark
1 code implementation • CVPR 2021 • Fan Zhang, Yu Li, ShaoDi You, Ying Fu
Based on this idea, we propose our method which can infer motion prior for single image low light video enhancement and enforce temporal consistency.
no code implementations • 18 May 2021 • Ryota Yoshihashi, Rei Kawakami, ShaoDi You, Tu Tuan Trinh, Makoto Iida, Takeshi Naemura
Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable.
no code implementations • 18 Dec 2020 • Yuxing Huang, ShaoDi You, Ying Fu, Qiu Shen
It is based on the idea that high-resolution HSIs in city scenes contain rich spectral information, which can be easily associated to semantics without manual labeling.
Semi-Supervised Semantic Segmentation Weakly supervised Semantic Segmentation +1
no code implementations • 18 Apr 2020 • Ruoteng Li, Xiaoyi Zhang, ShaoDi You, Yu Li
We select a large number of high-quality frames of real outdoor scenes and render haze on them using depth from stereo.
1 code implementation • ECCV 2020 • Wei Wang, ShaoDi You, Sezer Karaoglu, Theo Gevers
The experiments further show significant performance improvement of kinship verification when trained on the same dataset with more realistic distributions.
no code implementations • 17 Mar 2020 • Hao Yang, Dan Yan, Li Zhang, Dong Li, YunDa Sun, ShaoDi You, Stephen J. Maybank
It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions.
Ranked #28 on Skeleton Based Action Recognition on NTU RGB+D 120
2 code implementations • CVPR 2020 • Yunfei Liu, Yu Li, ShaoDi You, Feng Lu
Intrinsic image decomposition, which is an essential task in computer vision, aims to infer the reflectance and shading of the scene.
1 code implementation • 27 Jul 2019 • Yunfei Liu, Yu Li, ShaoDi You, Feng Lu
Reflection is common in images capturing scenes behind a glass window, which is not only a disturbance visually but also influence the performance of other computer vision algorithms.
no code implementations • 24 Jul 2019 • Shaodi You, Erqi Huang, Shuaizhe Liang, Yongrong Zheng, Yunxiang Li, Fan Wang, Sen Lin, Qiu Shen, Xun Cao, Diming Zhang, Yuanjiang Li, Yu Li, Ying Fu, Boxin Shi, Feng Lu, Yinqiang Zheng, Robby T. Tan
This document introduces the background and the usage of the Hyperspectral City Dataset and the benchmark.
no code implementations • 10 Jul 2019 • Tianxiu Yu, Shijie Zhang, Cong Lin, ShaoDi You, Jian Wu, Jiawan Zhang, Xiaohong Ding, Huili An
Follow the trend, we release the first public dataset for Dunhuang Grotto Painting restoration.
no code implementations • 18 Dec 2018 • Kenta Moriwaki, Ryota Yoshihashi, Rei Kawakami, ShaoDi You, Takeshi Naemura
It makes the reconstruction faithful to the input.
1 code implementation • CVPR 2019 • Ryota Yoshihashi, Wen Shao, Rei Kawakami, ShaoDi You, Makoto Iida, Takeshi Naemura
Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns.
no code implementations • 11 Dec 2018 • Lu Liu, Robby T. Tan, ShaoDi You
This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features.
no code implementations • 3 Sep 2018 • Zhixiang Hao, Yu Li, ShaoDi You, Feng Lu
However, depth estimation is a dense prediction problem and low-resolution feature maps usually generate blurred depth map which is undesirable in application.
1 code implementation • ECCV 2018 • Xiaofeng Han, Chuong Nguyen, ShaoDi You, Jianfeng Lu
Water bodies, such as puddles and flooded areas, on and off road pose significant risks to autonomous cars.
no code implementations • CVPR 2018 • Xiang Wang, ShaoDi You, Xi Li, Huimin Ma
Then in the top-down step, the refined object regions are used as supervision to train the segmentation network and to predict object masks.
General Classification Weakly supervised Semantic Segmentation +1
1 code implementation • COLING 2018 • Hongru Liang, Haozheng Wang, Jun Wang, ShaoDi You, Zhe Sun, Jin-Mao Wei, Zhenglu Yang
Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others.
no code implementations • 15 May 2018 • Seiichiro Fukuda, Ryota Yoshihashi, Rei Kawakami, ShaoDi You, Makoto Iida, Takeshi Naemura
We evaluated our proposed architecture on a combination of detection and segmentation using two datasets.
no code implementations • 16 Apr 2018 • Ziang Cheng, ShaoDi You, Viorela Ila, Hongdong Li
In experiments, we validate our ap- proach upon synthetic and real hazy images, where our method showed superior performance over state-of-the-art approaches, suggesting semantic information facilitates the haze removal task.
no code implementations • 8 Dec 2017 • Junxuan Li, ShaoDi You, Antonio Robles-Kelly
Moreover, the non-linearity in deep nets, often achieved by a rectifier unit, is here cast as a convolution in the frequency domain.
no code implementations • ECCV 2018 • Kaiyue Lu, ShaoDi You, Nick Barnes
Image smoothing is a fundamental task in computer vision, that aims to retain salient structures and remove insignificant textures.
no code implementations • 14 Sep 2017 • Ryota Yoshihashi, Tu Tuan Trinh, Rei Kawakami, ShaoDi You, Makoto Iida, Takeshi Naemura
While generic object detection has achieved large improvements with rich feature hierarchies from deep nets, detecting small objects with poor visual cues remains challenging.
no code implementations • 10 May 2017 • Riku Shigematsu, David Feng, ShaoDi You, Nick Barnes
Here we propose a novel deep CNN architecture for RGB-D salient object detection that exploits high-level, mid-level, and low level features.
no code implementations • 20 Dec 2016 • Shijie Zhang, Lizhen Qu, ShaoDi You, Zhenglu Yang, Jiawan Zhang
In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image.
no code implementations • ICCV 2017 • Zhichen Zhao, Huimin Ma, ShaoDi You
Second, for each body parts, a Part Action Res-Net is used to predict semantic body part actions.
no code implementations • 29 Aug 2016 • Xiang Wang, Huimin Ma, Xiaozhi Chen, ShaoDi You
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.
no code implementations • 21 Jul 2016 • Yu Li, ShaoDi You, Michael S. Brown, Robby T. Tan
This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes.
no code implementations • CVPR 2016 • David Feng, Nick Barnes, ShaoDi You, Chris McCarthy
Recent work in salient object detection has considered the incorporation of depth cues from RGB-D images.
Ranked #24 on RGB-D Salient Object Detection on NJU2K
no code implementations • 1 Jun 2016 • Shaodi You, Yasuyuki Matsushita, Sudipta Sinha, Yusuke Bou, Katsushi Ikeuchi
Digitally unwrapping images of paper sheets is crucial for accurate document scanning and text recognition.
no code implementations • 6 May 2016 • Shaodi You, Nick Barnes, Janine Walker
In this paper, we propose a color to grayscale image conversion algorithm (C2G) that aims to preserve the perceptual properties of the color image as much as possible.
no code implementations • 4 Apr 2016 • Shaodi You, Robby T. Tan, Rei Kawakami, Yasuhiro Mukaigawa, Katsushi Ikeuchi
(2) The imagery inside a water-drop is determined by the water-drop 3D shape and total reflection at the boundary.
no code implementations • CVPR 2013 • Shaodi You, Robby T. Tan, Rei Kawakami, Katsushi Ikeuchi
First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video.