36 citations · 102 across the 22 of their papers we have counts for
16 papers · 1 filter
Deep Light Pollution Removal in Night Cityscape Photographs
Hao Wang, Xiaolin Wu, Xi Zhang +1
Nighttime photography is severely degraded by light pollution induced by pervasive artificial lighting in urban environments. After long-range scattering and spatial diffusion, unw…
Anisotropic Pooling for LUT-realizable CNN Image Restoration
Xi Zhang, Xiaolin Wu
Table look-up realization of image restoration CNNs has the potential of achieving competitive image quality while being much faster and resource frugal than the straightforward CN…
Receptive Field Expanded Look-Up Tables for Vision Inference: Advancing from Low-level to High-level Tasks
Xi Zhang, Xiaolin Wu
Recently, several look-up table (LUT) methods were developed to greatly expedite the inference of CNNs in a classical strategy of trading space for speed. However, these LUT method…
Class-Invariant Test-Time Augmentation for Domain Generalization
Zhicheng Lin, Xiaolin Wu, Xi Zhang
Deep models often suffer significant performance degradation under distribution shifts. Domain generalization (DG) seeks to mitigate this challenge by enabling models to generalize…
Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization
Hao Xu, Xiaolin Wu, Xi Zhang
3D Gaussian Splatting (3DGS) is rapidly gaining popularity for its photorealistic rendering quality and real-time performance, but it generates massive amounts of data. Hence compr…
Learning Hierarchical Sparse Transform Coding for 3DGS Compression
Hao Xu, Xiaolin Wu, Xi Zhang
Current 3DGS compression methods largely forego the neural analysis-synthesis transform, which is a crucial component in learned signal compression systems. As a result, redundancy…
Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based Refinement
Hao Xu, Xi Zhang, Xiaolin Wu
Compressing a set of unordered points is far more challenging than compressing images/videos of regular sample grids, because of the difficulties in characterizing neighboring rela…
Multi-modality Deep Restoration of Extremely Compressed Face Videos
Xi Zhang, Xiaolin Wu
Arguably the most common and salient object in daily video communications is the talking head, as encountered in social media, virtual classrooms, teleconferences, news broadcastin…
Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton
Xi Zhang, Xiaolin Wu
We propose a deep learning system for attention-guided dual-layer image compression (AGDL). In the AGDL compression system, an image is encoded into two layers, a base layer and an…
Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos
Yanhui Guo, Xi Zhang, Xiaolin Wu
We propose a novel deep multi-modality neural network for restoring very low bit rate videos of talking heads. Such video contents are very common in social media, teleconferencing…
Challenge of Spatial Cognition for Deep Learning
Xi Zhang, Xiaolin Wu, Jun Du
Given the success of the deep convolutional neural networks (DCNNs) in applications of visual recognition and classification, it would be tantalizing to test if DCNNs can also lear…
Cognitive Deficit of Deep Learning in Numerosity
Xiaolin Wu, Xi Zhang, Xiao Shu
Subitizing, or the sense of small natural numbers, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic sk…
Near-lossless -constrained Image Decompression via Deep Neural Network
Xi Zhang, Xiaolin Wu
Recently a number of CNN-based techniques were proposed to remove image compression artifacts. As in other restoration applications, these techniques all learn a mapping from decom…
HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval
Xi Zhang, Siyu Zhou, Jiashi Feng +5
As the rapid growth of multi-modal data, hashing methods for cross-modal retrieval have received considerable attention. Deep-networks-based cross-modal hashing methods are appeali…
Automated Inference on Sociopsychological Impressions of Attractive Female Faces
Xiaolin Wu, Xi Zhang, Chang Liu
This article is a sequel to our earlier work [25]. The main objective of our research is to explore the potential of supervised machine learning in face-induced social computing an…
Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)
Xiaolin Wu, Xi Zhang
In November 2016 we submitted to arXiv our paper "Automated Inference on Criminality Using Face Images". It generated a great deal of discussions in the Internet and some media out…