Search Results for author: Qingsen Yan

Found 17 papers, 2 papers with code

Multiple Object Tracking based on Occlusion-Aware Embedding Consistency Learning

no code implementations5 Nov 2023 Yaoqi Hu, Axi Niu, Yu Zhu, Qingsen Yan, Jinqiu Sun, Yanning Zhang

The OPM predicts occlusion information for each true detection, facilitating the selection of valid samples for consistency learning of the track's visual embedding.

Multiple Object Tracking valid

Towards High-quality HDR Deghosting with Conditional Diffusion Models

no code implementations2 Nov 2023 Qingsen Yan, Tao Hu, Yuan Sun, Hao Tang, Yu Zhu, Wei Dong, Luc van Gool, Yanning Zhang

To address this challenge, we formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition, consisting of the feature condition generator and the noise predictor.

Denoising Image Generation

VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection

no code implementations22 Aug 2023 Peng Wu, Xuerong Zhou, Guansong Pang, Lingru Zhou, Qingsen Yan, Peng Wang, Yanning Zhang

Unlike current works that directly feed extracted features into the weakly supervised classifier for frame-level binary classification, VadCLIP makes full use of fine-grained associations between vision and language on the strength of CLIP and involves dual branch.

Anomaly Detection Binary Classification +1

All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation

no code implementations6 Aug 2023 Cheng Zhang, Yu Zhu, Qingsen Yan, Jinqiu Sun, Yanning Zhang

To address this issue, we propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) that can effectively capture and utilize accurate degradation representation for image restoration.

Contrastive Learning Deblurring +3

A Unified HDR Imaging Method with Pixel and Patch Level

no code implementations CVPR 2023 Qingsen Yan, Weiye Chen, Song Zhang, Yu Zhu, Jinqiu Sun, Yanning Zhang

The proposed HyHDRNet consists of a content alignment subnetwork and a Transformer-based fusion subnetwork.

GRAN: Ghost Residual Attention Network for Single Image Super Resolution

no code implementations28 Feb 2023 Axi Niu, Pei Wang, Yu Zhu, Jinqiu Sun, Qingsen Yan, Yanning Zhang

GRAB consists of the Ghost Module and Channel and Spatial Attention Module (CSAM) to alleviate the generation of redundant features.

Image Super-Resolution

Take a Prior from Other Tasks for Severe Blur Removal

no code implementations14 Feb 2023 Pei Wang, Danna Xue, Yu Zhu, Jinqiu Sun, Qingsen Yan, Sung-Eui Yoon, Yanning Zhang

For general scene deblurring, the feature space of the blurry image and corresponding sharp image under the high-level vision task is closer, which inspires us to rely on other tasks (e. g. classification) to learn a comprehensive prior in severe blur removal cases.

Deblurring Image Deblurring +1

Stock Market Prediction via Deep Learning Techniques: A Survey

no code implementations24 Dec 2022 Jinan Zou, Qingying Zhao, Yang Jiao, Haiyao Cao, Yanxi Liu, Qingsen Yan, Ehsan Abbasnejad, Lingqiao Liu, Javen Qinfeng Shi

Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods.

Stock Market Prediction

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration Vocal Bursts Intensity Prediction

Exploring and Evaluating Image Restoration Potential in Dynamic Scenes

1 code implementation CVPR 2022 Cheng Zhang, Shaolin Su, Yu Zhu, Qingsen Yan, Jinqiu Sun, Yanning Zhang

In this paper, to better study an image's potential value that can be explored for restoration, we propose a novel concept, referring to image restoration potential (IRP).

Image Restoration

Non-uniform Motion Deblurring with Blurry Component Divided Guidance

no code implementations15 Jan 2021 Pei Wang, Wei Sun, Qingsen Yan, Axi Niu, Rui Li, Yu Zhu, Jinqiu Sun, Yanning Zhang

To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree.

Blind Image Deblurring Image Deblurring +1

Attention-based network for low-light image enhancement

no code implementations20 May 2020 Cheng Zhang, Qingsen Yan, Yu Zhu, Xianjun Li, Jinqiu Sun, Yanning Zhang

Extensive experiments demonstrate the superiority of the proposed network in terms of suppressing the chromatic aberration and noise artifacts in enhancement, especially when the low-light image has severe noise.

Denoising Low-Light Image Enhancement

COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

no code implementations23 Apr 2020 Qingsen Yan, Bo wang, Dong Gong, Chuan Luo, Wei Zhao, Jianhu Shen, Qinfeng Shi, Shuo Jin, Liang Zhang, Zheng You

Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection.

Computed Tomography (CT) Image Segmentation +3

3D Gated Recurrent Fusion for Semantic Scene Completion

no code implementations17 Feb 2020 Yu Liu, Jie Li, Qingsen Yan, Xia Yuan, Chunxia Zhao, Ian Reid, Cesar Cadena

This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion.

3D Semantic Scene Completion Scene Understanding

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