Search Results for author:
Found 24 papers, 12 papers with code
Mobile Image Restoration via Prior Quantization
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.
Diffusion Action Segmentation
Temporal action segmentation is crucial for understanding long-form videos.
Ranked #2 on
Action Segmentation
on Breakfast
MUVA: A New Large-Scale Benchmark for Multi-View Amodal Instance Segmentation in the Shopping Scenario
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.
Real-World Deep Local Motion Deblurring
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
Medical image segmentation has been widely recognized as a pivot procedure for clinical diagnosis, analysis, and treatment planning.
ASFormer: Transformer for Action Segmentation
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 #2 on
Action Segmentation
on Assembly101
Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition
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.
Towards Unified Surgical Skill Assessment
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
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
Human Perception-based Evaluation Criterion for Ultra-high Resolution Cell Membrane Segmentation
Computer vision technology is widely used in biological and medical data analysis and understanding.
Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field
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
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
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
Blind Image Quality Assessment
No-Reference Image Quality Assessment
+1
Robust Medical Instrument Segmentation Challenge 2019
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.
LabelFool: A Trick in the Label Space
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
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
Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information
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
To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.
Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?
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.
Harmonic Adversarial Attack Method
The property of edge-free guarantees that the generated adversarial images can still preserve visual quality, even when perturbations are of large magnitudes.
Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification
Recognition of surgical gesture is crucial for surgical skill assessment and efficient surgery training.
Ranked #3 on
Action Segmentation
on JIGSAWS
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
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.
Unsupervised Ensemble Learning with Dependent Classifiers
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.
Quality Assessment for Comparing Image Enhancement Algorithms
As the image enhancement algorithms developed in recent years, how to compare the performances of different image enhancement algorithms becomes a novel task.
