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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces
Pierluigi Zama Ramirez;Fabio Tosi;Luigi Di Stefano;Radu Timofte;Alex Costanzino;Matteo Poggi;Samuele Salti;Stefano Mattoccia;Yangyang Zhang;Cailin Wu;Zhuangda He;Shuangshuang Yin;Jiaxu Dong;Yangchenxu Liu;Hao Jiang;Jun Shi;Yong A;Yixiang Jin;Dingzhe Li;Bingxin Ke;Anton Obukhov;Tinafu Wang;Nando Metzger;Shengyu Huang;Konrad Schindler;Yachuan Huang;Jiaqi Li;Junrui Zhang;Yiran Wang;Zihao Huang;Tianqi Liu;Zhiguo Cao;Pengzhi Li;Jui-Lin Wang;Wenjie Zhu;Hui Geng;Yuxin Zhang;Long Lan;Kele Xu;Tao Sun;Qisheng Xu;Sourav Saini;Aashray Gupta;Sahaj K. Mistry;Aryan Shukla;Vinit Jakhetiya;Sunil Jaiswal;Yuejin Sun;Zhuofan Zheng;Yi Ning;Jen-Hao Cheng;Hou-I Liu;Hsiang-Wei Huang;Cheng-Yen Yang;Zhongyu Jiang;Yi-Hao Peng;Aishi Huang;Jenq-Neng Hwang
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (16)
This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The cha...Show More
NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces
Pierluigi Zama Ramirez;Fabio Tosi;Luigi Di Stefano;Radu Timofte;Alex Costanzino;Matteo Poggi;Samuele Salti;Stefano Mattoccia;Yangyang Zhang;Cailin Wu;Zhuangda He;Shuangshuang Yin;Jiaxu Dong;Yangchenxu Liu;Hao Jiang;Jun Shi;Yong A;Yixiang Jin;Dingzhe Li;Bingxin Ke;Anton Obukhov;Tinafu Wang;Nando Metzger;Shengyu Huang;Konrad Schindler;Yachuan Huang;Jiaqi Li;Junrui Zhang;Yiran Wang;Zihao Huang;Tianqi Liu;Zhiguo Cao;Pengzhi Li;Jui-Lin Wang;Wenjie Zhu;Hui Geng;Yuxin Zhang;Long Lan;Kele Xu;Tao Sun;Qisheng Xu;Sourav Saini;Aashray Gupta;Sahaj K. Mistry;Aryan Shukla;Vinit Jakhetiya;Sunil Jaiswal;Yuejin Sun;Zhuofan Zheng;Yi Ning;Jen-Hao Cheng;Hou-I Liu;Hsiang-Wei Huang;Cheng-Yen Yang;Zhongyu Jiang;Yi-Hao Peng;Aishi Huang;Jenq-Neng Hwang
Year: 2024 | Conference Paper |
2nd Workshop on Maritime Computer Vision (MaCVi) 2024: Challenge Results
Benjamin Kiefer;Lojze Žust;Matej Kristan;Janez Perš;Matija Teršek;Arnold Wiliem;Martin Messmer;Cheng-Yen Yang;Hsiang-Wei Huang;Zhongyu Jiang;Heng-Cheng Kuo;Jie Mei;Jenq-Neng Hwang;Daniel Stadler;Lars Sommer;Kaer Huang;Aiguo Zheng;Weitu Chong;Kanokphan Lertniphonphan;Jun Xie;Feng Chen;Jian Li;Zhepeng Wang;Luca Zedda;Andrea Loddo;Cecilia Di Ruberto;Tuan-Anh Vu;Hai Nguyen-Truong;Tan-Sang Ha;Quan-Dung Pham;Sai-Kit Yeung;Yuan Feng;Nguyen Thanh Thien;Lixin Tian;Sheng-Yao Kuan;Yuan-Hao Ho;Angel Bueno Rodriguez;Borja Carrillo-Perez;Alexander Klein;Antje Alex;Yannik Steiniger;Felix Sattler;Edgardo Solano-Carrillo;Matej Fabijanić;Magdalena Šumunec;Nadir Kapetanović;Andreas Michel;Wolfgang Gross;Martin Weinmann
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-ideruification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obst...Show More
2nd Workshop on Maritime Computer Vision (MaCVi) 2024: Challenge Results
Benjamin Kiefer;Lojze Žust;Matej Kristan;Janez Perš;Matija Teršek;Arnold Wiliem;Martin Messmer;Cheng-Yen Yang;Hsiang-Wei Huang;Zhongyu Jiang;Heng-Cheng Kuo;Jie Mei;Jenq-Neng Hwang;Daniel Stadler;Lars Sommer;Kaer Huang;Aiguo Zheng;Weitu Chong;Kanokphan Lertniphonphan;Jun Xie;Feng Chen;Jian Li;Zhepeng Wang;Luca Zedda;Andrea Loddo;Cecilia Di Ruberto;Tuan-Anh Vu;Hai Nguyen-Truong;Tan-Sang Ha;Quan-Dung Pham;Sai-Kit Yeung;Yuan Feng;Nguyen Thanh Thien;Lixin Tian;Sheng-Yao Kuan;Yuan-Hao Ho;Angel Bueno Rodriguez;Borja Carrillo-Perez;Alexander Klein;Antje Alex;Yannik Steiniger;Felix Sattler;Edgardo Solano-Carrillo;Matej Fabijanić;Magdalena Šumunec;Nadir Kapetanović;Andreas Michel;Wolfgang Gross;Martin Weinmann
Year: 2024 | Conference Paper |
ESA: Expert-and-Samples-Aware Incremental Learning Under Longtail Distribution
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Most works in class incremental learning (CIL) assume disjoint sets of classes as tasks. Although a few works deal with overlapped sets of classes, they either assume a balanced data distribution or assume a mild imbalanced distribution. Instead, in this paper, we explore one of the understudied real-world CIL settings where (1) different tasks can share some classes but with new data samples, and...Show More
Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (13)
When applying a pre-trained 2D-to-3D human pose lifting model to a target unseen dataset, large performance degradation is commonly encountered due to domain shift issues. We observe that the degradation is caused by two factors: 1) the large distribution gap over global positions of poses between the source and target datasets due to variant camera parameters and settings, and 2) the deficient di...Show More
Progressive Mixup Augmented Teacher-Student Learning for Unsupervised Domain Adaptation
Aotian Zheng;Jie Mei;Farron Wallace;Craig Rose;Rania Hussein;Jenq-Neng Hwang
2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain, mostly through learning a domain invariant feature representation. Currently, the best performing UDA methods use category level domain alignment to capture fine-grained information, resulting in significantly improved performance over global alignment. While successf...Show More
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