Search Results for author: Xue Zhang

Found 12 papers, 4 papers with code

TransportationGames: Benchmarking Transportation Knowledge of (Multimodal) Large Language Models

no code implementations9 Jan 2024 Xue Zhang, Xiangyu Shi, Xinyue Lou, Rui Qi, Yufeng Chen, Jinan Xu, Wenjuan Han

Large language models (LLMs) and multimodal large language models (MLLMs) have shown excellent general capabilities, even exhibiting adaptability in many professional domains such as law, economics, transportation, and medicine.

Benchmarking

A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation

1 code implementation20 Oct 2023 Xue Zhang, Songming Zhang, Yunlong Liang, Yufeng Chen, Jian Liu, Wenjuan Han, Jinan Xu

Furthermore, for situations requiring multiple paraphrases for each source sentence, we design a Diverse Templates Search (DTS) algorithm, which can enhance the diversity between paraphrases without sacrificing quality.

Data Augmentation Paraphrase Generation +2

TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection

1 code implementation26 May 2023 Xue Zhang, Xiao-Han Zhang, Jiacheng Ying, Zehua Sheng, Heng Yu, Chunguang Li, Hui-Liang Shen

In this paper, we propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet.

Pedestrian Detection

Manifold Graph Signal Restoration using Gradient Graph Laplacian Regularizer

no code implementations9 Jun 2022 Fei Chen, Gene Cheung, Xue Zhang

In this paper, focusing on manifold graphs -- collections of uniform discrete samples on low-dimensional continuous manifolds -- we generalize GLR to gradient graph Laplacian regularizer (GGLR) that promotes planar / piecewise planar (PWP) signal reconstruction.

Graph Embedding

Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation

1 code implementation NAACL 2022 Siyu Lai, Zhen Yang, Fandong Meng, Xue Zhang, Yufeng Chen, Jinan Xu, Jie zhou

Generating adversarial examples for Neural Machine Translation (NMT) with single Round-Trip Translation (RTT) has achieved promising results by releasing the meaning-preserving restriction.

Machine Translation NMT +1

Fast Computation of Generalized Eigenvectors for Manifold Graph Embedding

no code implementations15 Dec 2021 Fei Chen, Gene Cheung, Xue Zhang

Experiments show that our embedding is among the fastest in the literature, while producing the best clustering performance for manifold graphs.

Clustering Graph Embedding

Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement

no code implementations9 Nov 2021 Xue Zhang, Gene Cheung, Jiahao Pang, Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan

Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization.

Graph Learning Image Denoising +1

FocusNet: Classifying Better by Focusing on Confusing Classes

2 code implementations14 Oct 2021 Xue Zhang, Zehua Sheng, Hui-Liang Shen

We also introduce a novel focus-picking loss function to improve classification accuracy by encouraging FocusNet to focus on the most confusing classes.

Classification Image Classification +1

Battery characterization via eddy-current imaging with nitrogen-vacancy centers in diamond

no code implementations22 Feb 2021 Xue Zhang, Georgios Chatzidrosos, Yinan Hu, Huijie Zheng, Arne Wickenbrock, Alexej Jerschow, Dmitry Budker

Sensitive and accurate diagnostic technologies with magnetic sensors are of great importance for identifying and localizing defects of rechargeable solid batteries in a noninvasive detection.

Applied Physics

Fast & Robust Image Interpolation using Gradient Graph Laplacian Regularizer

no code implementations25 Jan 2021 Fei Chen, Gene Cheung, Xue Zhang

In the graph signal processing (GSP) literature, it has been shown that signal-dependent graph Laplacian regularizer (GLR) can efficiently promote piecewise constant (PWC) signal reconstruction for various image restoration tasks.

Image Restoration

DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning

no code implementations15 Feb 2020 Xue Zhang, Wangxin Xiao, Weijia Xiao

Results: We proposed a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network.

BIG-bench Machine Learning Drug Discovery +1

How to Train Triplet Networks with 100K Identities?

no code implementations9 Sep 2017 Chong Wang, Xue Zhang, Xipeng Lan

However, as the number of identities becomes extremely large, the training will suffer from bad local minima because effective hard triplets are difficult to be found.

Face Recognition Image Retrieval +1

Cannot find the paper you are looking for? You can Submit a new open access paper.