Search Results for author:
Found 11 papers, 9 papers with code
Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement
Subsequently, we propose an objective quality assessment measure that plays a critical role in bridging the gap between visual quality and enhancement.
DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models.
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment
The underlying mechanism of the proposed approach is based upon the mild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion.
Beyond Visual Field of View: Perceiving 3D Environment with Echoes and Vision
This paper focuses on perceiving and navigating 3D environments using echoes and RGB image.
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
In particular, we create a large-scale database for QUality assessment Of The Enhanced LOw-Light Image (QUOTE-LOL), which serves as the foundation in studying and developing objective quality assessment measures.
V-SlowFast Network for Efficient Visual Sound Separation
The objective of this paper is to perform visual sound separation: i) we study visual sound separation on spectrograms of different temporal resolutions; ii) we propose a new light yet efficient three-stream framework V-SlowFast that operates on Visual frame, Slow spectrogram, and Fast spectrogram.
No-Reference Image Quality Assessment by Hallucinating Pristine Features
In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination.
Visually Guided Sound Source Separation and Localization using Self-Supervised Motion Representations
The objective of this paper is to perform audio-visual sound source separation, i. e.~to separate component audios from a mixture based on the videos of sound sources.
Optical Flow Estimation
Visually Guided Sound Source Separation
Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment
In this work, we propose a no-reference video quality assessment method, aiming to achieve high-generalization capability in cross-content, -resolution and -frame rate quality prediction.
Leveraging Category Information for Single-Frame Visual Sound Source Separation
Furthermore, our models are able to exploit the information of the sound source category in the separation process.
Visually Guided Sound Source Separation using Cascaded Opponent Filter Network
A key element in COF is a novel opponent filter module that identifies and relocates residual components between sources.
