Search Results for author:
Found 40 papers, 18 papers with code
Optimizing LiDAR Placements for Robust Driving Perception in Adverse Conditions
The robustness of driving perception systems under unprecedented conditions is crucial for safety-critical usages.
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property
In this paper, we contribute a new benchmark, the first Multilingual-oriented quiZ on Intellectual Property (MoZIP), for the evaluation of LLMs in the IP domain.
Customizable Perturbation Synthesis for Robust SLAM Benchmarking
To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations.
Graph-of-Thought: Utilizing Large Language Models to Solve Complex and Dynamic Business Problems
This paper presents Graph-of-Thought (GoT), a new model for workflow automation that enhances the flexibility and efficiency of Large Language Models (LLMs) in complex task execution.
Efficient Discrete Physics-informed Neural Networks for Addressing Evolutionary Partial Differential Equations
Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning.
ShareCMP: Polarization-Aware RGB-P Semantic Segmentation
Multimodal semantic segmentation is developing rapidly, but the modality of RGB-Polarization remains underexplored.
Ranked #1 on
Semantic Segmentation
on ZJU-RGB-P
TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction
In TTMFN, we present a two-stream multimodal co-attention transformer module to take full advantage of the complex relationships between different modalities and the potential connections within the modalities.
Valuation Duration of the Stock Market
In contrast, at the height of the global financial crisis, more than 2. 2% of market value is from dividends in the next year, implying a duration of 46 years.
Lightweight Full-Convolutional Siamese Tracker
Although single object trackers have achieved advanced performance, their large-scale models hinder their application on limited resources platforms.
UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection
In this paper, we propose a plug-and-play Underwater joint image enhancement Module (UnitModule) that provides the input image preferred by the detector.
OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting
Extensive experiments on real-world benchmark datasets for downstream time series anomaly detection and forecasting tasks demonstrate that OneShotSTL is from 10 to over 1, 000 times faster than the state-of-the-art methods, while still providing comparable or even better accuracy.
DAA: A Delta Age AdaIN operation for age estimation via binary code transformer
Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the style map of each age through the learned values representing the mean and standard deviation.
Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features.
Interactive Log Parsing via Light-weight User Feedback
Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications.
VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum
Therefore, based on VQNet 1. 0, we further propose VQNet 2. 0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization.
Underwater Object Tracker: UOSTrack for Marine Organism Grasping of Underwater Vehicles
The UOHT training paradigm is designed to train the sample-imbalanced underwater tracker so that the tracker is exposed to a great number of underwater domain training samples and learns the feature expressions.
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have flocked to Transformer-based networks for various main stream medical tasks such as classification, segmentation, and estimation.
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits
And models of functional addiction circuits developed from functional imaging are an effective tool for discovering and verifying addiction circuits.
Physics-guided Data Augmentation for Learning the Solution Operator of Linear Differential Equations
Neural networks, especially the recent proposed neural operator models, are increasingly being used to find the solution operator of differential equations.
Bi-LSTM Price Prediction based on Attention Mechanism
In this paper, we propose a bidirectional LSTM neural network based on an attention mechanism, which is based on two popular assets, gold and bitcoin.
VI-PINNs: Variance-involved Physics-informed Neural Networks for Fast and Accurate Prediction of Partial Differential Equations
Although physics-informed neural networks(PINNs) have progressed a lot in many real applications recently, there remains problems to be further studied, such as achieving more accurate results, taking less training time, and quantifying the uncertainty of the predicted results.
Contextual Transformer for Offline Meta Reinforcement Learning
Firstly, we propose prompt tuning for offline RL, where a context vector sequence is concatenated with the input to guide the conditional policy generation.
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising
In this work, we proposed an efficient spatial and channel-wise encoder-decoder transformer, Spach Transformer, that can leverage spatial and channel information based on local and global MSAs.
LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding
LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS anomaly detection methods to support fast troubleshooting in cloud computing, micro-service systems, etc.
A Noise-level-aware Framework for PET Image Denoising
Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only.
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond.
TransMorph: Transformer for unsupervised medical image registration
Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications.
Ranked #1 on
Medical Image Registration
on OASIS
Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks
The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT.
ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image.
Ranked #4 on
Medical Image Registration
on OASIS
A Multi-task Joint Framework for Real-time Person Search
To solve these problems, we propose a Multi-task Joint Framework for real-time person search (MJF), which optimizes the person detection, feature extraction and identity comparison respectively.
Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization
In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering.
Quality-aware semi-supervised learning for CMR segmentation
SemiQCSeg can be an efficient approach for training segmentation networks for medical image data when labelled datasets are scarce.
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
In this paper, we identify that the main bottleneck is in the training mechanisms, where the negative instances used in training are not representative of the irrelevant documents in testing.
Ranked #7 on
Passage Retrieval
on Natural Questions
Boundary-aware Context Neural Network for Medical Image Segmentation
In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information.
Cascaded Context Enhancement Network for Automatic Skin Lesion Segmentation
In this paper, we formulate a cascaded context enhancement neural network for automatic skin lesion segmentation.
Triplet Online Instance Matching Loss for Person Re-identification
Triplet loss processes batch construction in a complicated and fussy way and converges slowly.
Acoustic Scene Classification Using Bilinear Pooling on Time-liked and Frequency-liked Convolution Neural Network
Although works have been done in using HPSS as input representation for CNN model in ASC task, this paper further investigate the possibility on leveraging the separated harmonic component and percussive component by curating 2 CNNs which tries to understand harmonic audio and percussive audio in their natural form, one specialized in extracting deep features in time biased domain and another specialized in extracting deep features in frequency biased domain, respectively.
Generating Anthropomorphic Phantoms Using Fully Unsupervised Deformable Image Registration with Convolutional Neural Networks
In this work, we present a novel image registration method for creating highly anatomically detailed anthropomorphic phantoms from a single digital phantom.
Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes
The database, containing PWs from 4, 374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data.
