52 citations · 192 across the 20 of their papers we have counts for
20 papers
When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron
Xuerui Qiu, Zheng Luan, Zhaorui Wang +1
Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. However, most existing SNN-based methods for image tasks…
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Xuerui Qiu, Rui-Jie Zhu, Yuhong Chou +3
Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional artificial neural networks (ANNs) due to their unique spike-based event-driven nature.…
Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System
Qihang Zhao, Rui-jie Zhu, Liu Yang +3
In the realm of search systems, multi-stage cascade architecture is a prevalent method, typically consisting of sequential modules such as matching, pre-ranking, and ranking. It is…
RWKV: Reinventing RNNs for the Transformer Era
Bo Peng, Eric Alcaide, Quentin Anthony +31
Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence leng…
Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation
Liwen Wu, Rui Zhu, Mustafa B. Yaldiz +7
Inverse path tracing has recently been applied to joint material and lighting estimation, given geometry and multi-view HDR observations of an indoor scene. However, it has two maj…
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
Rui-Jie Zhu, Qihang Zhao, Guoqi Li +1
As the size of large language models continue to scale, so does the computational resources required to run it. Spiking Neural Networks (SNNs) have emerged as an energy-efficient a…
Research on work zone vehicle queuing behavior based on cellular automata
Shao-Yuan Chen, Li-Xian Zhong, Rui-Xi Zhu +3
A model is proposed to estimate the work zone queue length, and the cellular automata based on empirical data is used for model validation. This estimation model can be applied to…
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
Yu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy +5
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input im…
IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes
Rui Zhu, Zhengqin Li, Janarbek Matai +2
Indoor scenes exhibit significant appearance variations due to myriad interactions between arbitrarily diverse object shapes, spatially-changing materials, and complex lighting. Sh…
TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks
Rui-Jie Zhu, Malu Zhang, Qihang Zhao +3
Spiking Neural Networks (SNNs) are attracting widespread interest due to their biological plausibility, energy efficiency, and powerful spatio-temporal information representation a…
Physically-Based Editing of Indoor Scene Lighting from a Single Image
Zhengqin Li, Jia Shi, Sai Bi +6
We present a method to edit complex indoor lighting from a single image with its predicted depth and light source segmentation masks. This is an extremely challenging problem that…
SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network
Cheng Jin, Rui-Jie Zhu, Xiao Wu +1
Spiking Neural Networks (SNNs) have piqued researchers' interest because of their capacity to process temporal information and low power consumption. However, current state-of-the-…
Improving Contrastive Learning by Visualizing Feature Transformation
Rui Zhu, Bingchen Zhao, Jingen Liu +2
Contrastive learning, which aims at minimizing the distance between positive pairs while maximizing that of negative ones, has been widely and successfully applied in unsupervised…
Learning Efficient Photometric Feature Transform for Multi-view Stereo
Kaizhang Kang, Cihui Xie, Ruisheng Zhu +4
We present a novel framework to learn to convert the perpixel photometric information at each view into spatially distinctive and view-invariant low-level features, which can be pl…
Bayesian ROC surface estimation under verification bias
Rui Zhu, Subhashis Ghosal
The Receiver Operating Characteristic (ROC) surface is a generalization of ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories. A co…
Learning Depth from Monocular Videos using Direct Methods
Chaoyang Wang, Jose Miguel Buenaposada, Rui Zhu +1
The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community. Unsupervised strategies to learning are partic…
Semantic Photometric Bundle Adjustment on Natural Sequences
Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin +2
The problem of obtaining dense reconstruction of an object in a natural sequence of images has been long studied in computer vision. Classically this problem has been solved throug…
Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin +2
Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision. Classical Structure from Motion (SfM) methods have attempted to solve thi…
Rethinking Reprojection: Closing the Loop for Pose-aware ShapeReconstruction from a Single Image
Rui Zhu, Hamed Kiani Galoogahi, Chaoyang Wang +1
An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image. Hitherto, the problem has been addressed through the application…
The Conditional Lucas & Kanade Algorithm
Chen-Hsuan Lin, Rui Zhu, Simon Lucey
The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense image and object alignment. The approach is efficient as it attempts to model the connection between a…