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Found 37 papers, 6 papers with code
面向垂直领域的阅读理解数据增强方法(Method for reading comprehension data enhancement in vertical field)
阅读理解问答系统是利用语义理解等自然语言处理技术, 根据输入问题, 对非结构化文档数据进行分析, 生成一个答案, 具有很高的研究和应用价值。在垂直领域应用过程中, 阅读理解问答数据标注成本高且用户问题表达复杂多样, 使得阅读理解问答系统准确率低、鲁棒性差。针对这一问题, 本文提出一种面向垂直领域的阅读理解问答数据的增强方法, 该方法基于真实用户问题, 构造阅读理解训练数据, 一方面降低标注成本, 另一方面增加训练数据多样性, 提升模型的准确率和鲁棒性。本文用汽车领域数据对该方法进行实验验证, 其结果表明该方法对垂直领域阅读理解模型的准确率和鲁棒性均能有效提升。
AdaViPro: Region-based Adaptive Visual Prompt for Large-Scale Models Adapting
Recently, prompt-based methods have emerged as a new alternative `parameter-efficient fine-tuning' paradigm, which only fine-tunes a small number of additional parameters while keeping the original model frozen.
GAN Based Near-Field Channel Estimation for Extremely Large-Scale MIMO Systems
The mixed line-of-sight (LoS) and non-line-of-sight (NLoS) XL-MIMO near-field channel model is adopted to describe the XL-MIMO near-field channel accurately.
Exploring Iterative Refinement with Diffusion Models for Video Grounding
In this paper, we propose DiffusionVG, a novel framework with diffusion models that formulates video grounding as a conditional generation task, where the target span is generated from Gaussian noise inputs and interatively refined in the reverse diffusion process.
SSLCL: An Efficient Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in Conversations
To address these challenges, we propose an efficient and model-agnostic SCL framework named Supervised Sample-Label Contrastive Learning with Soft-HGR Maximal Correlation (SSLCL), which eliminates the need for a large batch size and can be seamlessly integrated with existing ERC models without introducing any model-specific assumptions.
Query-dominant User Interest Network for Large-Scale Search Ranking
The overall historical behaviors are various but noisy while search behaviors are always sparse.
Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers
Although dominant in natural language processing, transformer-based models remain challenged by the task of long-sequence processing, because the computational cost of self-attention operations in transformers swells quadratically with the input sequence length.
High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage
Visual inspection is predominantly used to evaluate the state of civil structures, but recent developments in unmanned aerial vehicles (UAVs) and artificial intelligence have increased the speed, safety, and reliability of the inspection process.
TransFusion: A Practical and Effective Transformer-based Diffusion Model for 3D Human Motion Prediction
Predicting human motion plays a crucial role in ensuring a safe and effective human-robot close collaboration in intelligent remanufacturing systems of the future.
Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes
Additionally these methods are reliant on the availability of large training datasets to converge to useful solutions.
ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images
Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images.
Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images.
LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation
Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts.
New starting point registration method for tagged MRI tongue motion estimation
Accurate tongue motion estimation is essential for tongue function evaluation.
Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer
Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.
CyberLoc: Towards Accurate Long-term Visual Localization
Second, a single-image-based localization pipeline (retrieval--matching--PnP) is performed to estimate 6-DoF camera poses for each query image, one for each 3D map.
Cepstral Coefficients for Earthquake Damage Assessment of Bridges Leveraging Deep Learning
The developed strategy for spatio-temporal analysis of signals enhances the robustness of damage diagnosis frameworks that utilize deep learning for monitoring lifeline structures.
High-Fidelity Visual Structural Inspections through Transformers and Learnable Resizers
Visual inspection is the predominant technique for evaluating the condition of civil infrastructure.
Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy
We found that DL-based direct segmentation on CBCT trained with pseudo labels and without influencer volumes shows poor performance compared to DIR-based segmentation.
Modality-Balanced Embedding for Video Retrieval
This modality imbalanceresults from a) modality gap: the relevance between a query and a video text is much easier to learn as the query is also a piece of text, with the same modality as the video text; b) data bias: most training samples can be solved solely by text matching.
Dressi: A Hardware-Agnostic Differentiable Renderer with Reactive Shader Packing and Soft Rasterization
HardSoftRas, our novel rendering process, is designed for inverse rendering with a graphics pipeline.
Region Specific Optimization (RSO)-based Deep Interactive Registration
A test time optimization (TTO) technique was proposed to further improve the DL models' performance.
Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
Firstly, we trained a population model with 200 patients, and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models.
Whole Brain Segmentation with Full Volume Neural Network
To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume.
Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
To overcome this challenge, we propose a generic new approach that bridges the gap between image-conditional and recent modulated unconditional generative architectures via co-modulation of both conditional and stochastic style representations.
Ranked #3 on
Image Inpainting
on FFHQ 512 x 512
Nonlinear Cooperative Control of Double Drone-Bar Transportation System
Due to the limitation of the drone's load capacity, various specific tasks need to be accomplished by multiple drones in collaboration.
Dynamical Systems Systems and Control Systems and Control
A Nonparametric Unsupervised Learning Approach for Structural Damage Detection
In this study, a novel unsupervised learning approach for SHM is proposed.
Deep Bayesian U-Nets for Efficient, Robust and Reliable Post-Disaster Damage Localization
It is shown that class variance correlates well with locations where the model makes mistakes.
RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning
Therefore, in order to enhance the reliability of sensing applications, apart from the physical phenomena/processes of interest, we believe it is also highly important to monitor the reliability of sensors and clean the sensor data before analysis on them being conducted.
Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with Cone-Beam Computed Tomography (CBCT) to Computed Tomography (CT) image conversion
We trained a model on CBCT images acquired from one vendor's scanners for head and neck cancer patients and applied it to images from another vendor's scanners and for other disease sites.
Model Uncertainty Quantification for Reliable Deep Vision Structural Health Monitoring
This paper proposes Bayesian inference for deep vision SHM models where uncertainty can be quantified using the Monte Carlo dropout sampling.
Intensity-Based Feature Selection for Near Real-Time Damage Diagnosis of Building Structures
This paper proposes a robust framework to build a damage prediction model for building structures.
AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community Forums
In order to address this task, we propose a system based on the BERT model with meta information of questions.
A Convolutional Cost-Sensitive Crack Localization Algorithm for Automated and Reliable RC Bridge Inspection
In the last decade, camera-equipped unmanned aerial vehicles (UAVs) have been widely used for visual inspections; however, the task of automatically extracting useful information from raw images is still challenging.
Generating Synthesized Computed Tomography (CT) from Cone-Beam Computed Tomography (CBCT) using CycleGAN for Adaptive Radiation Therapy
Dose calculation accuracy using sCT images has been improved over the original CBCT images, with the average Gamma Index passing rate increased from 95. 4% to 97. 4% for 1 mm/1% criteria.
Medical Physics
An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering
Then, instead of merging the sequence into a single vector with pooling operation, soft alignments between words from the question and the relation are learned.
A deep learning-based method for relative location prediction in CT scan images
Relative location prediction in computed tomography (CT) scan images is a challenging problem.
