Search Results for author: Tingting Jiang

Found 24 papers, 12 papers with code

Mobile Image Restoration via Prior Quantization

no code implementations10 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.

Image Restoration Quantization

MUVA: A New Large-Scale Benchmark for Multi-View Amodal Instance Segmentation in the Shopping Scenario

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.

Amodal Instance Segmentation Segmentation +1

Real-World Deep Local Motion Deblurring

no code implementations18 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.


Contrastive and Selective Hidden Embeddings for Medical Image Segmentation

1 code implementation21 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.

Contrastive Learning feature selection +4

ASFormer: Transformer for Action Segmentation

1 code implementation16 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.

Action Segmentation Segmentation

Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition

2 code implementations10 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.

Online surgical phase recognition

Towards Unified Surgical Skill Assessment

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.

Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training

1 code implementation9 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.

Video Quality Assessment

Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field

no code implementations27 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.

Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion

1 code implementation27 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.


Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment

1 code implementation10 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.

Blind Image Quality Assessment No-Reference Image Quality Assessment +1

LabelFool: A Trick in the Label Space

no code implementations25 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.

Quality Assessment of In-the-Wild Videos

2 code implementations1 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.

Image Classification Video Quality Assessment

Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information

1 code implementation19 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 No-Reference Image Quality Assessment Multimedia

Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images

1 code implementation18 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.

Blind Image Quality Assessment Image Quality Estimation +1

Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?

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.

Blind Image Quality Assessment Image Classification +2

Harmonic Adversarial Attack Method

no code implementations18 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.

Adversarial Attack

DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network

4 code implementations2 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.

Feature Engineering Predicting Patient Outcomes +2

Unsupervised Ensemble Learning with Dependent Classifiers

no code implementations20 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.

Ensemble Learning

Quality Assessment for Comparing Image Enhancement Algorithms

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.

Image Enhancement Image Quality Assessment

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