Search Results for author: King Ngi Ngan

Found 11 papers, 3 papers with code

Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration

no code implementations27 Nov 2023 Lei Wang, Qingbo Wu, Desen Yuan, King Ngi Ngan, Hongliang Li, Fanman Meng, Linfeng Xu

Learning based image quality assessment (IQA) models have obtained impressive performance with the help of reliable subjective quality labels, where mean opinion score (MOS) is the most popular choice.

Image Quality Assessment

Forgetting to Remember: A Scalable Incremental Learning Framework for Cross-Task Blind Image Quality Assessment

1 code implementation15 Sep 2022 Rui Ma, Qingbo Wu, King Ngi Ngan, Hongliang Li, Fanman Meng, Linfeng Xu

More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other.

Blind Image Quality Assessment Incremental Learning

Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning

1 code implementation5 Apr 2021 Haoran Wei, Qingbo Wu, Hui Li, King Ngi Ngan, Hongliang Li, Fanman Meng, Linfeng Xu

In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning.

Image Dehazing Single Image Dehazing

BA^2M: A Batch Aware Attention Module for Image Classification

no code implementations28 Mar 2021 Qishang Cheng, Hongliang Li, Qingbo Wu, King Ngi Ngan

Then, we feed the SARs of the whole batch to a normalization function to get the weights for each sample.

Classification General Classification +1

Advanced Geometry Surface Coding for Dynamic Point Cloud Compression

no code implementations11 Mar 2021 Jian Xiong, Hao Gao, Miaohui Wang, Hongliang Li, King Ngi Ngan, Weisi Lin

In video-based dynamic point cloud compression (V-PCC), 3D point clouds are projected onto 2D images for compressing with the existing video codecs.

Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking

no code implementations6 May 2019 Lu Sheng, Jianfei Cai, Tat-Jen Cham, Vladimir Pavlovic, King Ngi Ngan

In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations.

Face Model Pose Estimation +1

MVF-Net: Multi-View 3D Face Morphable Model Regression

1 code implementation CVPR 2019 Fanzi Wu, Linchao Bao, Yajing Chen, Yonggen Ling, Yibing Song, Songnan Li, King Ngi Ngan, Wei Liu

The main ingredient of the view alignment loss is a differentiable dense optical flow estimator that can backpropagate the alignment errors between an input view and a synthetic rendering from another input view, which is projected to the target view through the 3D shape to be inferred.

Optical Flow Estimation regression

Hierarchy Neighborhood Discriminative Hashing for An Unified View of Single-Label and Multi-Label Image retrieval

no code implementations10 Jan 2019 Lei Ma, Hongliang Li, Qingbo Wu, Fanman Meng, King Ngi Ngan

Finally, we propose a hierarchy neighborhood discriminative hashing loss to unify the single-label and multilabel image retrieval problem with a one-stream deep neural network architecture.

Multi-Label Image Retrieval Retrieval +2

3D Facial Expression Reconstruction using Cascaded Regression

no code implementations10 Dec 2017 Fanzi Wu, Songnan Li, Tianhao Zhao, King Ngi Ngan, Lv Sheng

2D landmarks are detected and used to initialize the 3D shape and mapping matrices.

regression

A Generative Model for Depth-Based Robust 3D Facial Pose Tracking

no code implementations CVPR 2017 Lu Sheng, Jianfei Cai, Tat-Jen Cham, Vladimir Pavlovic, King Ngi Ngan

We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations.

Face Model Pose Estimation +1

Motion-Depth: RGB-D Depth Map Enhancement with Motion and Depth in Complement

no code implementations CVPR 2014 Tak-Wai Hui, King Ngi Ngan

Since the spatial resolution of the color image is generally higher than that of the depth image, this paper introduces a new method to enhance the depth images captured by a moving RGB-D system using the depth cues from the induced optical flow.

3D Reconstruction Image Enhancement +1

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