Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024

no code implementations • 2 Jun 2024 • Biao Wu, Diankai Zhang, Si Gao, Chengjian Zheng, Shaoli Liu, Ning Wang

Pixel-level Scene Understanding is one of the fundamental problems in computer vision, which aims at recognizing object classes, masks and semantics of each pixel in the given image.

2nd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation

no code implementations • 1 Jun 2024 • Biao Wu, Diankai Zhang, Si Gao, Chengjian Zheng, Shaoli Liu, Ning Wang

In order to deal with the task of video panoptic segmentation in the wild, we propose a robust integrated video panoptic segmentation solution.

Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

2 code implementations • 25 Apr 2024 • Marcos V. Conde, Zhijun Lei, Wen Li, Cosmin Stejerean, Ioannis Katsavounidis, Radu Timofte, Kihwan Yoon, Ganzorig Gankhuyag, Jiangtao Lv, Long Sun, Jinshan Pan, Jiangxin Dong, Jinhui Tang, Zhiyuan Li, Hao Wei, Chenyang Ge, Dongyang Zhang, Tianle Liu, Huaian Chen, Yi Jin, Menghan Zhou, Yiqiang Yan, Si Gao, Biao Wu, Shaoli Liu, Chengjian Zheng, Diankai Zhang, Ning Wang, Xintao Qiu, Yuanbo Zhou, Kongxian Wu, Xinwei Dai, Hui Tang, Wei Deng, Qingquan Gao, Tong Tong, Jae-Hyeon Lee, Ui-Jin Choi, Min Yan, Xin Liu, Qian Wang, Xiaoqian Ye, Zhan Du, Tiansen Zhang, Long Peng, Jiaming Guo, Xin Di, Bohao Liao, Zhibo Du, Peize Xia, Renjing Pei, Yang Wang, Yang Cao, ZhengJun Zha, Bingnan Han, Hongyuan Yu, Zhuoyuan Wu, Cheng Wan, Yuqing Liu, Haodong Yu, Jizhe Li, Zhijuan Huang, Yuan Huang, Yajun Zou, Xianyu Guan, Qi Jia, Heng Zhang, Xuanwu Yin, Kunlong Zuo, Hyeon-Cheol Moon, Tae-hyun Jeong, Yoonmo Yang, Jae-Gon Kim, Jinwoo Jeong, Sunjei Kim

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs.

4k Image Super-Resolution

Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing

no code implementations • 5 Jun 2023 • Biao Wu, Shaoli Liu, Diankai Zhang, Chengjian Zheng, Si Gao, Xiaofeng Zhang, Ning Wang

Pixel-level Scene Understanding is one of the fundamental problems in computer vision, which aims at recognizing object classes, masks and semantics of each pixel in the given image.

Semantic Segmentation Video Semantic Segmentation

Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report

no code implementations • 7 Nov 2022 • Andrey Ignatov, Radu Timofte, Cheng-Ming Chiang, Hsien-Kai Kuo, Yu-Syuan Xu, Man-Yu Lee, Allen Lu, Chia-Ming Cheng, Chih-Cheng Chen, Jia-Ying Yong, Hong-Han Shuai, Wen-Huang Cheng, Zhuang Jia, Tianyu Xu, Yijian Zhang, Long Bao, Heng Sun, Diankai Zhang, Si Gao, Shaoli Liu, Biao Wu, Xiaofeng Zhang, Chengjian Zheng, Kaidi Lu, Ning Wang, Xiao Sun, HaoDong Wu, Xuncheng Liu, Weizhan Zhang, Caixia Yan, Haipeng Du, Qinghua Zheng, Qi Wang, Wangdu Chen, Ran Duan, Mengdi Sun, Dan Zhu, Guannan Chen, Hojin Cho, Steve Kim, Shijie Yue, Chenghua Li, Zhengyang Zhuge, Wei Chen, Wenxu Wang, Yufeng Zhou, Xiaochen Cai, Hengxing Cai, Kele Xu, Li Liu, Zehua Cheng, Wenyi Lian, Wenjing Lian

While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.

Video Super-Resolution

NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

2 code implementations • 20 Apr 2022 • Ren Yang, Radu Timofte, Meisong Zheng, Qunliang Xing, Minglang Qiao, Mai Xu, Lai Jiang, Huaida Liu, Ying Chen, Youcheng Ben, Xiao Zhou, Chen Fu, Pei Cheng, Gang Yu, Junyi Li, Renlong Wu, Zhilu Zhang, Wei Shang, Zhengyao Lv, Yunjin Chen, Mingcai Zhou, Dongwei Ren, Kai Zhang, WangMeng Zuo, Pavel Ostyakov, Vyal Dmitry, Shakarim Soltanayev, Chervontsev Sergey, Zhussip Magauiya, Xueyi Zou, Youliang Yan, Pablo Navarrete Michelini, Yunhua Lu, Diankai Zhang, Shaoli Liu, Si Gao, Biao Wu, Chengjian Zheng, Xiaofeng Zhang, Kaidi Lu, Ning Wang, Thuong Nguyen Canh, Thong Bach, Qing Wang, Xiaopeng Sun, Haoyu Ma, Shijie Zhao, Junlin Li, Liangbin Xie, Shuwei Shi, Yujiu Yang, Xintao Wang, Jinjin Gu, Chao Dong, Xiaodi Shi, Chunmei Nian, Dong Jiang, Jucai Lin, Zhihuai Xie, Mao Ye, Dengyan Luo, Liuhan Peng, Shengjie Chen, Qian Wang, Xin Liu, Boyang Liang, Hang Dong, Yuhao Huang, Kai Chen, Xingbei Guo, Yujing Sun, Huilei Wu, Pengxu Wei, Yulin Huang, Junying Chen, Ik Hyun Lee, Sunder Ali Khowaja, Jiseok Yoon

This challenge includes three tracks.

Super-Resolution

Distilling Object Detectors with Feature Richness

1 code implementation • NeurIPS 2021 • Zhixing Du, Rui Zhang, Ming Chang, Xishan Zhang, Shaoli Liu, Tianshi Chen, Yunji Chen

Second, these methods imitate some features which are mistakenly regarded as the background by the teacher detector.

Knowledge Distillation Model Compression +1

Domain-Specific Suppression for Adaptive Object Detection

no code implementations • CVPR 2021 • Yu Wang, Rui Zhang, Shuo Zhang, Miao Li, Yangyang Xia, Xishan Zhang, Shaoli Liu

The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one.

Domain Adaptation Object +2

DWM: A Decomposable Winograd Method for Convolution Acceleration

no code implementations • 3 Feb 2020 • Di Huang, Xishan Zhang, Rui Zhang, Tian Zhi, Deyuan He, Jiaming Guo, Chang Liu, Qi Guo, Zidong Du, Shaoli Liu, Tianshi Chen, Yunji Chen

In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.

Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

no code implementations • 1 Nov 2019 • Xishan Zhang, Shaoli Liu, Rui Zhang, Chang Liu, Di Huang, Shiyi Zhou, Jiaming Guo, Yu Kang, Qi Guo, Zidong Du, Yunji Chen

Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers.

Image Classification Machine Translation +2

BENCHIP: Benchmarking Intelligence Processors

no code implementations • 23 Oct 2017 • Jinhua Tao, Zidong Du, Qi Guo, Huiying Lan, Lei Zhang, Shengyuan Zhou, Lingjie Xu, Cong Liu, Haifeng Liu, Shan Tang, Allen Rush, Willian Chen, Shaoli Liu, Yunji Chen, Tianshi Chen

The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware).

Benchmarking