In this paper, we propose a plug-and-play Underwater joint image enhancement Module (UnitModule) that provides the input image preferred by the detector.
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.
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.
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.
Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications.
no code implementations • 9 Jan 2023 • Huanyu Bian, Zhilong Jia, Menghan Dou, Yuan Fang, Lei LI, Yiming Zhao, Hanchao Wang, Zhaohui Zhou, Wei Wang, Wenyu Zhu, Ye Li, Yang Yang, Weiming Zhang, Nenghai Yu, Zhaoyun Chen, Guoping Guo
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.
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.
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.
And models of functional addiction circuits developed from functional imaging are an effective tool for discovering and verifying addiction circuits.
Neural networks, especially the recent proposed neural operator models, are increasingly being used to find the solution operator of differential equations.
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.
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.
Firstly, we propose prompt tuning for offline RL, where a context vector sequence is concatenated with the input to guide the conditional policy generation.
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 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.
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.
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond.
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.
The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT.
However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image.
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.
In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering.
SemiQCSeg can be an efficient approach for training segmentation networks for medical image data when labelled datasets are scarce.
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
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.
In this paper, we formulate a cascaded context enhancement neural network for automatic skin lesion segmentation.
Triplet loss processes batch construction in a complicated and fussy way and converges slowly.
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.
In this work, we present a novel image registration method for creating highly anatomically detailed anthropomorphic phantoms from a single digital phantom.
The database, containing PWs from 4, 374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data.
Based on the detected ground, the optimal walkable direction is computed and the user is then informed via converted beep sound.