Search Results for author:
Found 21 papers, 11 papers with code
Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks
This problem is caused by the pathological distribution of the neural tangent kernel's (NTK's) eigenvalues of coordinate networks.
Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography
The integration of miniaturized spectrometers into mobile devices offers new avenues for image quality enhancement and facilitates novel downstream tasks.
Encoding Semantic Priors into the Weights of Implicit Neural Representation
Specifically, SPW uses the Semantic Neural Network (SNN) to extract both low- and high-level semantic information of the target visual signal and generates the semantic vector, which is input into the Weight Generation Network (WGN) to generate the weights of INR model.
MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors
To validate the dataset, we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework, VP-MoCap, for human motion capture.
Batch Normalization Alleviates the Spectral Bias in Coordinate Networks
Representing signals using coordinate networks dominates the area of inverse problems recently and is widely applied in various scientific computing tasks.
RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets
We propose Re-parameterized Refocusing Convolution (RefConv) as a replacement for regular convolutional layers, which is a plug-and-play module to improve the performance without any inference costs.
Learn to Enhance the Negative Information in Convolutional Neural Network
In this way, LENI can enhance the model representational capacity significantly while maintaining the original advantages of ReLU.
FalconNet: Factorization for the Light-weight ConvNets
To address these issues, we factorize the four vital components of light-weight CNNs from coarse to fine and redesign them: i) we design a light-weight overall architecture termed LightNet, which obtains better performance by simply implementing the basic blocks of other light-weight CNNs; ii) we abstract a Meta Light Block, which consists of spatial operator and channel operator and uniformly describes current basic blocks; iii) we raise RepSO which constructs multiple spatial operator branches to enhance the representational ability; iv) we raise the concept of receptive range, guided by which we raise RefCO to sparsely factorize the channel operator.
Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline
We endeavor on a rarely explored task named Insubstantial Object Detection (IOD), which aims to localize the object with following characteristics: (1) amorphous shape with indistinct boundary; (2) similarity to surroundings; (3) absence in color.
FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction
By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input.
Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition
Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity.
Facial Expression Recognition
Facial Expression Recognition (FER)
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image
It is based on the idea that high-resolution HSIs in city scenes contain rich spectral information, which can be easily associated to semantics without manual labeling.
FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction
In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input.
Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling
This paper proposes a novel Non-Local Attention optmization and Improved Context modeling-based image compression (NLAIC) algorithm, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure.
Learned Point Cloud Geometry Compression
This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a. k. a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE).
Hyperspectral City V1.0 Dataset and Benchmark
This document introduces the background and the usage of the Hyperspectral City Dataset and the benchmark.
Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications
Networked video applications, e. g., video conferencing, often suffer from poor visual quality due to unexpected network fluctuation and limited bandwidth.
Non-local Attention Optimized Deep Image Compression
This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure.
Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization
We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate.
Gated Context Model with Embedded Priors for Deep Image Compression
Besides, a field study on perceptual quality is also given via a dedicated subjective assessment, to compare the efficiency of our proposed methods and other conventional image compression methods.
Deep Image Compression via End-to-End Learning
We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate.
