no code implementations • 26 Aug 2024 • Ruohua Shi, Qiufan Pang, Lei Ma, Lingyu Duan, Tiejun Huang, Tingting Jiang
Electron microscopy (EM) imaging offers unparalleled resolution for analyzing neural tissues, crucial for uncovering the intricacies of synaptic connections and neural processes fundamental to understanding behavioral mechanisms.
no code implementations • 2 Aug 2024 • Ruohua Shi, Zhaochen Liu, Lingyu Duan, Tingting Jiang
Additionally, we evaluate several leading amodal segmentation methods to establish a benchmark for this new dataset.
no code implementations • 25 May 2024 • Zhaochen Liu, Limeng Qiao, Xiangxiang Chu, Tingting Jiang
In the point level, we introduce the concept of uncertainty to explicitly assist the model in identifying and focusing on ambiguous points.
no code implementations • 20 Apr 2024 • Chenxi Yang, Yujia Liu, Dingquan Li, Yan Zhong, Tingting Jiang
Meanwhile, it is important to note that the correlation, like ranking correlation, plays a significant role in NR-IQA tasks.
1 code implementation • CVPR 2024 • Yujia Liu, Chenxi Yang, Dingquan Li, Jianhao Ding, Tingting Jiang
To be specific, we present theoretical evidence showing that the magnitude of score changes is related to the $\ell_1$ norm of the model's gradient with respect to the input image.
no code implementations • 10 Jan 2024 • Chenxi Yang, Yujia Liu, Dingquan Li, Tingting Jiang
Ensuring the robustness of NR-IQA methods is vital for reliable comparisons of different image processing techniques and consistent user experiences in recommendations.
no code implementations • 3 Jan 2024 • Zhaochen Liu, Zhixuan Li, Tingting Jiang
We present a novel solution to tackle this problem by introducing a directed expansion approach from visible masks to corresponding amodal masks.
no code implementations • IEEE Transactions on Multimedia 2023 • Zhixuan Li, Weining Ye, Tingting Jiang, Tiejun Huang
In human amodal perception, shape-prior knowledge is helpful for AIS.
no code implementations • IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023 • Zhixuan Li, Ruohua Shi, Tiejun Huang, Tingting Jiang
Hence we believe it is vital for the method to be distinguishable about the degree of occlusion for each instance.
no code implementations • 10 May 2023 • Shiqi Chen, Jinwen Zhou, Menghao Li, Yueting Chen, Tingting Jiang
In digital images, the performance of optical aberration is a multivariate degradation, where the spectral of the scene, the lens imperfections, and the field of view together contribute to the results.
1 code implementation • ICCV 2023 • Daochang Liu, Qiyue Li, AnhDung Dinh, Tingting Jiang, Mubarak Shah, Chang Xu
Temporal action segmentation is crucial for understanding long-form videos.
Ranked #3 on
Action Segmentation
on GTEA
no code implementations • ICCV 2023 • Zhixuan Li, Weining Ye, Juan Terven, Zachary Bennett, Ying Zheng, Tingting Jiang, Tiejun Huang
To bridge this gap, we propose a new task called Multi-view Amodal Instance Segmentation (MAIS) and introduce the MUVA dataset, the first MUlti-View AIS dataset that takes the shopping scenario as instantiation.
no code implementations • European Conference on Computer Vision (ECCV) 2022 • Zhixuan Li, Weining Ye, Tingting Jiang, Tiejun Huang
However, masks in 2D space are only some observations and samples from the 3D model in different viewpoints and thus can not represent the real complete physical shape of the instances.
no code implementations • 18 Apr 2022 • Haoying Li, Ziran Zhang, Tingting Jiang, Peng Luo, Huajun Feng, Zhihai Xu
Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements.
1 code implementation • 21 Jan 2022 • Zhuowei Li, Zihao Liu, Zhiqiang Hu, Qing Xia, Ruiqin Xiong, Shaoting Zhang, Dimitris Metaxas, Tingting Jiang
Medical image segmentation has been widely recognized as a pivot procedure for clinical diagnosis, analysis, and treatment planning.
1 code implementation • 16 Oct 2021 • Fangqiu Yi, Hongyu Wen, Tingting Jiang
However, there are several major concerns when directly applying the Transformer to the action segmentation task, such as the lack of inductive biases with small training sets, the deficit in processing long input sequence, and the limitation of the decoder architecture to utilize temporal relations among multiple action segments to refine the initial predictions.
Ranked #3 on
Action Segmentation
on Assembly101
2 code implementations • 10 Jul 2021 • Fangqiu Yi, Tingting Jiang
To address the problem, we propose a new non end-to-end training strategy and explore different designs of multi-stage architecture for surgical phase recognition task.
no code implementations • CVPR 2021 • Daochang Liu, Qiyue Li, Tingting Jiang, Yizhou Wang, Rulin Miao, Fei Shan, Ziyu Li
In this paper, a unified multi-path framework for automatic surgical skill assessment is proposed, which takes care of multiple composing aspects of surgical skills, including surgical tool usage, intraoperative event pattern, and other skill proxies.
1 code implementation • 9 Nov 2020 • Dingquan Li, Tingting Jiang, Ming Jiang
We focus on automatically assessing the quality of in-the-wild videos, which is a challenging problem due to the absence of reference videos, the complexity of distortions, and the diversity of video contents.
Ranked #1 on
Video Quality Assessment
on MSU NR VQA Database
no code implementations • 16 Oct 2020 • Ruohua Shi, Wenyao Wang, Zhixuan Li, Liuyuan He, Kaiwen Sheng, Lei Ma, Kai Du, Tingting Jiang, Tiejun Huang
Computer vision technology is widely used in biological and medical data analysis and understanding.
1 code implementation • 27 Aug 2020 • Daochang Liu, Yuhui Wei, Tingting Jiang, Yizhou Wang, Rulin Miao, Fei Shan, Ziyu Li
In the experiments on the binary instrument segmentation task of the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset, the proposed method achieves 0. 71 IoU and 0. 81 Dice score without using a single manual annotation, which is promising to show the potential of unsupervised learning for surgical tool segmentation.
no code implementations • 27 Aug 2020 • Daochang Liu, Tingting Jiang, Yizhou Wang, Rulin Miao, Fei Shan, Ziyu Li
Then an objective and automated framework based on neural network is proposed to predict surgical skills through the proxy of COF.
1 code implementation • 10 Aug 2020 • Dingquan Li, Tingting Jiang, Ming Jiang
Experiments on two relevant datasets (KonIQ-10k and CLIVE) show that, compared to MAE or MSE loss, the new loss enables the IQA model to converge about 10 times faster and the final model achieves better performance.
Ranked #2 on
Image Quality Assessment
on MSU NR VQA Database
no code implementations • 23 Mar 2020 • Tobias Ross, Annika Reinke, Peter M. Full, Martin Wagner, Hannes Kenngott, Martin Apitz, Hellena Hempe, Diana Mindroc Filimon, Patrick Scholz, Thuy Nuong Tran, Pierangela Bruno, Pablo Arbeláez, Gui-Bin Bian, Sebastian Bodenstedt, Jon Lindström Bolmgren, Laura Bravo-Sánchez, Hua-Bin Chen, Cristina González, Dong Guo, Pål Halvorsen, Pheng-Ann Heng, Enes Hosgor, Zeng-Guang Hou, Fabian Isensee, Debesh Jha, Tingting Jiang, Yueming Jin, Kadir Kirtac, Sabrina Kletz, Stefan Leger, Zhixuan Li, Klaus H. Maier-Hein, Zhen-Liang Ni, Michael A. Riegler, Klaus Schoeffmann, Ruohua Shi, Stefanie Speidel, Michael Stenzel, Isabell Twick, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Wang, Yu-Jie Zhang, Yan-Jie Zhou, Lei Zhu, Manuel Wiesenfarth, Annette Kopp-Schneider, Beat P. Müller-Stich, Lena Maier-Hein
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
no code implementations • 25 Sep 2019 • Yujia Liu, Tingting Jiang, Ming Jiang
It is widely known that well-designed perturbations can cause state-of-the-art machine learning classifiers to mis-label an image, with sufficiently small perturbations that are imperceptible to the human eyes.
2 code implementations • 1 Aug 2019 • Dingquan Li, Tingting Jiang, Ming Jiang
We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network.
Ranked #6 on
Video Quality Assessment
on MSU NR VQA Database
1 code implementation • 19 Oct 2018 • Qin He, Dingquan Li, Tingting Jiang, Ming Jiang
So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model.
Blind Image Quality Assessment
Multimedia
1 code implementation • 18 Oct 2018 • Dingquan Li, Tingting Jiang, Ming Jiang
To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.
1 code implementation • IEEE Transactions on Multimedia 2018 • Dingquan Li, Tingting Jiang, Weisi Lin, Ming Jiang
The proposed method, SFA, is compared with nine representative blur-specific NR-IQA methods, two general-purpose NR-IQA methods, and two extra full-reference IQA methods on Gaussian blur images (with and without Gaussian noise/JPEG compression) and realistic blur images from multiple databases, including LIVE, TID2008, TID2013, MLIVE1, MLIVE2, BID, and CLIVE.
no code implementations • 18 Jul 2018 • Wen Heng, Shuchang Zhou, Tingting Jiang
The property of edge-free guarantees that the generated adversarial images can still preserve visual quality, even when perturbations are of large magnitudes.
1 code implementation • 21 Jun 2018 • Daochang Liu, Tingting Jiang
Recognition of surgical gesture is crucial for surgical skill assessment and efficient surgery training.
Ranked #3 on
Action Segmentation
on JIGSAWS
4 code implementations • 2 Jun 2016 • Jared Katzman, Uri Shaham, Jonathan Bates, Alexander Cloninger, Tingting Jiang, Yuval Kluger
We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations.
no code implementations • 20 Oct 2015 • Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger
In unsupervised ensemble learning, one obtains predictions from multiple sources or classifiers, yet without knowing the reliability and expertise of each source, and with no labeled data to assess it.
no code implementations • CVPR 2014 • Zhengying Chen, Tingting Jiang, Yonghong Tian
As the image enhancement algorithms developed in recent years, how to compare the performances of different image enhancement algorithms becomes a novel task.