Search Results for author: Song Zhang

Found 16 papers, 2 papers with code

Learning to See in the Dark with Events

no code implementations ECCV 2020 Song Zhang, Yu Zhang, Zhe Jiang, Dongqing Zou, Jimmy Ren, Bin Zhou

A detail enhancing branch is proposed to reconstruct day light-specific features from the domain-invariant representations in a residual manner, regularized by a ranking loss.

Representation Learning Unsupervised Domain Adaptation

TiC: Exploring Vision Transformer in Convolution

1 code implementation6 Oct 2023 Song Zhang, Qingzhong Wang, Jiang Bian, Haoyi Xiong

While models derived from Vision Transformers (ViTs) have been phonemically surging, pre-trained models cannot seamlessly adapt to arbitrary resolution images without altering the architecture and configuration, such as sampling the positional encoding, limiting their flexibility for various vision tasks.

Image Classification

ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth interval

no code implementations17 Aug 2023 Song Zhang, Wenjia Xu, Zhiwei Wei, Lili Zhang, Yang Wang, Junyi Liu

Moreover, our method also achieves the lowest $e_{1}$ and $e_{3}$ on the BlendedMVS dataset and the highest Acc and $F_{1}$-score on the ETH 3D dataset, surpassing all listed methods. Project website: https://github. com/zs670980918/ARAI-MVSNet

Stereo Depth Estimation

Pixel-wise rational model for structured light system

no code implementations11 May 2023 Raúl Vargas, Lenny A. Romero, Song Zhang, Andres G. Marrugo

This Letter presents a novel structured light system model that effectively considers local lens distortion by pixel-wise rational functions.

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.

TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning

1 code implementation28 Dec 2022 Peixiang Huang, Li Liu, Renrui Zhang, Song Zhang, Xinli Xu, Baichao Wang, Guoyi Liu

In this paper, we propose the learning scheme of Target Inner-Geometry from the LiDAR modality into camera-based BEV detectors for both dense depth and BEV features, termed as TiG-BEV.

3D Object Detection object-detection

Self-Supervised Intensity-Event Stereo Matching

no code implementations1 Nov 2022 Jinjin Gu, Jinan Zhou, Ringo Sai Wo Chu, Yan Chen, Jiawei Zhang, Xuanye Cheng, Song Zhang, Jimmy S. Ren

Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption.

Self-Supervised Learning Stereo Matching

A Polyphone BERT for Polyphone Disambiguation in Mandarin Chinese

no code implementations1 Jul 2022 Song Zhang, Ken Zheng, Xiaoxu Zhu, Baoxiang Li

Grapheme-to-phoneme (G2P) conversion is an indispensable part of the Chinese Mandarin text-to-speech (TTS) system, and the core of G2P conversion is to solve the problem of polyphone disambiguation, which is to pick up the correct pronunciation for several candidates for a Chinese polyphonic character.

Polyphone disambiguation

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

Practical Adoption of Cloud Computing in Power Systems- Drivers, Challenges, Guidance, and Real-world Use Cases

no code implementations31 Jul 2021 Song Zhang, Amritanshu Pandey, Xiaochuan Luo, Maggy Powell, Ranjan Banerji, Lei Fan, Abhineet Parchure, Edgardo Luzcando

Motivated by The Federal Energy Regulatory Commission's (FERC) recent direction and ever-growing interest in cloud adoption by power utilities, a Task Force was established to assist power system practitioners with secure, reliable and cost-effective adoption of cloud technology to meet various business needs.

Cloud Computing

Unperturbed inverse kinematics nucleon knockout measurements with a 48 GeV/c carbon beam

no code implementations4 Feb 2021 M. Patsyuk, J. Kahlbow, G. Laskaris, M. Duer, V. Lenivenko, E. P. Segarra, T. Atovullaev, G. Johansson, T. Aumann, A. Corsi, O. Hen, M. Kapishin, V. Panin, E. Piasetzky, Kh. Abraamyan, S. Afanasiev, G. Agakishiev, P. Alekseev, E. Atkin, T. Aushev, V. Babkin, V. Balandin, D. Baranov, N. Barbashina, P. Batyuk, S. Bazylev, A. Beck, C. A. Bertulani, D. Blaschke, D. Blau, D. Bogoslovsky, A. Bolozdynya, K. Boretzky, V. Burtsev, M. Buryakov, S. Buzin, A. Chebotov, J. Chen, A. Ciszewski, R. Cruz-Torres, B. Dabrowska, D. Dabrowski A. Dmitriev, A. Dryablov, P. Dulov, D. Egorov, A. Fediunin, I. Filippov, K. Filippov, D. Finogeev, I. Gabdrakhmanov, A. Galavanov, I. Gasparic, O. Gavrischuk, K. Gertsenberger, A. Gillibert, V. Golovatyuk, M. Golubeva, F. Guber, Yu. Ivanova, A. Ivashkin, A. Izvestnyy, S. Kakurin, V. Karjavin, N. Karpushkin, R. Kattabekov, V. Kekelidze, S. Khabarov, Yu. Kiryushin, A. Kisiel, V. Kolesnikov, A. Kolozhvari, Yu. Kopylov, I. Korover, L. Kovachev, A. Kovalenko, Yu. Kovalev, A. Kugler, S. Kuklin, E. Kulish, A. Kuznetsov, E. Ladygin, N. Lashmanov, E. Litvinenko, S. Lobastov, B. Loher, Y. -G. Ma, A. Makankin, A. Maksymchyuk, A. Malakhov, I. Mardor, S. Merts, A. Morozov, S. Morozov, G. Musulmanbekov, R. Nagdasev, D. Nikitin, V. Palchik, D. Peresunko, M. Peryt, O. Petukhov, Yu. Petukhov, S. Piyadin, V. Plotnikov, G. Pokatashkin, Yu. Potrebenikov, O. Rogachevsky, V. Rogov, K. Roslon, D. Rossi, I. Rufanov, P. Rukoyatkin, M. Rumyantsev, D. Sakulin, V. Samsonov, H. Scheit, A. Schmidt, S. Sedykh, I. Selyuzhenkov, P. Senger, S. Sergeev, A. Shchipunov, A. Sheremeteva, M. Shitenkov, V. Shumikhin, A. Shutov, V. Shutov, H. Simon, I. Slepnev, V. Slepnev, I. Slepov, A. Sorin, V. Sosnovtsev, V. Spaskov, T. Starecki, A. Stavinskiy, E. Streletskaya, O. Streltsova, M. Strikhanov, N. Sukhov, D. Suvarieva, J. Tanaka, A. Taranenko, N. Tarasov, O. Tarasov, V. Tarasov, A. Terletsky, O. Teryaev, V. Tcholakov, V. Tikhomirov, A. Timoshenko, N. Topilin, B. Topko, H. Tornqvist, I. Tyapkin, V. Vasendina, A. Vishnevsky, N. Voytishin, V. Wagner, O. Warmusz, I. Yaron, V. Yurevich, N. Zamiatin, Song Zhang, E. Zherebtsova, V. Zhezher, N. Zhigareva, A. Zinchenko, E. Zubarev, M. Zuev

Measuring the microscopic structure of such systems is a formidable challenge, often met by particle knockout scattering experiments.

Nuclear Experiment Nuclear Theory

Self-Supervised Learning Aided Class-Incremental Lifelong Learning

no code implementations10 Jun 2020 Song Zhang, Gehui Shen, Jinsong Huang, Zhi-Hong Deng

Lifelong or continual learning remains to be a challenge for artificial neural network, as it is required to be both stable for preservation of old knowledge and plastic for acquisition of new knowledge.

Class Incremental Learning Incremental Learning +1

Generative Feature Replay with Orthogonal Weight Modification for Continual Learning

no code implementations7 May 2020 Gehui Shen, Song Zhang, Xiang Chen, Zhi-Hong Deng

For this scenario, generative replay is a promising strategy which generates and replays pseudo data for previous tasks to alleviate catastrophic forgetting.

Class Incremental Learning Incremental Learning

Deep learning for smart fish farming: applications, opportunities and challenges

no code implementations6 Apr 2020 Xinting Yang, Song Zhang, Jintao Liu, Qinfeng Gao, Shuanglin Dong, Chao Zhou

This change can create new opportunities and a series of challenges for information and data processing in smart fish farming.

Decision Making

Hybrid calibration procedure for fringe projection profilometry based on stereo-vision and polynomial fitting

no code implementations9 Mar 2020 Raul Vargas, Andres G. Marrugo, Song Zhang, Lenny A. Romero

The key to accurate 3D shape measurement in Fringe Projection Profilometry (FPP) is the proper calibration of the measurement system.

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