Search Results for author:
Found 30 papers, 22 papers with code
Towards Explainable In-the-Wild Video Quality Assessment: a Database and a Language-Prompted Approach
Though subjective studies have collected overall quality scores for these videos, how the abstract quality scores relate with specific factors is still obscure, hindering VQA methods from more concrete quality evaluations (e. g. sharpness of a video).
Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment
The proliferation of videos collected during in-the-wild natural settings has pushed the development of effective Video Quality Assessment (VQA) methodologies.
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
Recent learning-based video quality assessment (VQA) algorithms are expensive to implement due to the cost of data collection of human quality opinions, and are less robust across various scenarios due to the biases of these opinions.
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
In light of this, we propose the Disentangled Objective Video Quality Evaluator (DOVER) to learn the quality of UGC videos based on the two perspectives.
Ranked #1 on
Video Quality Assessment
on LIVE-VQC
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
On the other hand, existing practices, such as resizing and cropping, will change the quality of original videos due to the loss of details and contents, and are therefore harmful to quality assessment.
Ranked #2 on
Video Quality Assessment
on KoNViD-1k
(using extra training data)
Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment
Experiments show that the perceptual representation in the HVS is an effective way of predicting subjective temporal quality, and thus TPQI can, for the first time, achieve comparable performance to the spatial quality metric and be even more effective in assessing videos with large temporal variations.
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration
Night imaging with modern smartphone cameras is troublesome due to low photon count and unavoidable noise in the imaging system.
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
Consisting of fragments and FANet, the proposed FrAgment Sample Transformer for VQA (FAST-VQA) enables efficient end-to-end deep VQA and learns effective video-quality-related representations.
Ranked #2 on
Video Quality Assessment
on LIVE-FB LSVQ
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment
Based on prominent time-series modeling ability of transformers, we propose a novel and effective transformer-based VQA method to tackle these two issues.
Ranked #5 on
Video Quality Assessment
on KoNViD-1k
ChildPredictor: A Child Face Prediction Framework with Disentangled Learning
On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors.
Age-Invariant Face Recognition
Image-to-Image Translation
+1
Dual-Camera Super-Resolution with Aligned Attention Modules
We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and high-fidelity results.
A Categorized Reflection Removal Dataset with Diverse Real-world Scenes
Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly.
MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition
MODNet is easy to be trained in an end-to-end manner.
Ranked #1 on
Image Matting
on PPM-100
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
Although SSL methods have achieved impressive results in image classification, the performances of applying them to pixel-wise tasks are unsatisfactory due to their need for dense outputs.
Hierarchical Regression Network for Spectral Reconstruction from RGB Images
Hyperspectral reconstruction from RGB images denotes a reverse process of hyperspectral imaging by discovering an inverse response function.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
Polarized Reflection Removal with Perfect Alignment in the Wild
We present a novel formulation to removing reflection from polarized images in the wild.
Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning
In this work, we show that the coupled EMA teacher causes a performance bottleneck.
Semi-Supervised Image Classification
Unsupervised Domain Adaptation
Deep Surface Normal Estimation with Hierarchical RGB-D Fusion
The growing availability of commodity RGB-D cameras has boosted the applications in the field of scene understanding.
DSR: Direct Self-rectification for Uncalibrated Dual-lens Cameras
Our method is evaluated on both real-istic and synthetic stereo image pairs, and produces supe-rior results compared to the calibrated rectification or otherself-rectification approaches
Image Dehazing using Bilinear Composition Loss Function
In this paper, we introduce a bilinear composition loss function to address the problem of image dehazing.
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement.
Robust Tracking Using Region Proposal Networks
Recent advances in visual tracking showed that deep Convolutional Neural Networks (CNN) trained for image classification can be strong feature extractors for discriminative trackers.
Accurate Single Stage Detector Using Recurrent Rolling Convolution
In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification
This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable.
Shepard Convolutional Neural Networks
In this paper, we draw on Shepard interpolation and design Shepard Convolutional Neural Networks (ShCNN) which efficiently realizes end-to-end trainable TVI operators in the network.
Hierarchical Saliency Detection on Extended CSSD
Complex structures commonly exist in natural images.
Dense Scattering Layer Removal
We propose a new model, together with advanced optimization, to separate a thick scattering media layer from a single natural image.
Hierarchical Saliency Detection
When dealing with objects with complex structures, saliency detection confronts a critical problem namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns.
