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Found 28 papers, 7 papers with code
Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task
And their experiments show that leveraging the answer summaries helps to attend the essential information in original lengthy answers and improve the answer selection performance under certain circumstances.
BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation
3) Current SAM-based models rely on external prompts, which cannot achieve automatic segmentation and, to some extent, affect diagnostic efficiency. To address these issues, we have developed a large-scale fully automated segmentation model specifically designed for brain lesion segmentation, named BrainSegDMLF.
Variational Autoencoder Framework for Hyperspectral Retrievals (Hyper-VAE) of Phytoplankton Absorption and Chlorophyll a in Coastal Waters for NASA's EMIT and PACE Missions
Phytoplankton absorb and scatter light in unique ways, subtly altering the color of water, changes that are often minor for human eyes to detect but can be captured by sensitive ocean color instruments onboard satellites from space.
Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm
Mixed service mode docks enhance efficiency by flexibly handling both loading and unloading trucks in warehouses.
BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection
To address these issues, we develop the BFA-YOLO model and the BFA-3D dataset in this study.
Enhancing Terrestrial Net Primary Productivity Estimation with EXP-CASA: A Novel Light Use Efficiency Model Approach
The EXP-CASA model effectively improves the CASA model by using novel functions for estimating the fraction of absorbed photosynthetically active radiation (FPAR) and environmental stress, by utilizing long-term observational data from FLUXNET and MODIS surface reflectance data.
GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation
Pre-trained language models have become an integral component of question-answering systems, achieving remarkable performance.
LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing
Addressing this gap, we propose a novel \textbf{L}ightweight \textbf{M}ultimodal data \textbf{F}usion \textbf{Net}work (LMFNet) to accomplish the tasks of fusion and semantic segmentation of multimodal remote sensing images.
Ranked #1 on
Semantic Segmentation
on Potsdam
LitSim: A Conflict-aware Policy for Long-term Interactive Traffic Simulation
Simulation is pivotal in evaluating the performance of autonomous driving systems due to the advantages of high efficiency and low cost compared to on-road testing.
Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation Strategies
Traditional supervised learning methods have historically encountered certain constraints in medical image segmentation due to the challenging collection process, high labeling cost, low signal-to-noise ratio, and complex features characterizing biomedical images.
S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar Imagery
Stereo matching and semantic segmentation are significant tasks in binocular satellite 3D reconstruction.
Segment Change Model (SCM) for Unsupervised Change detection in VHR Remote Sensing Images: a Case Study of Buildings
The field of Remote Sensing (RS) widely employs Change Detection (CD) on very-high-resolution (VHR) images.
Indeterminate Probability Neural Network
Besides, for our proposed neural network framework, the output of neural network is defined as probability events, and based on the statistical analysis of these events, the inference model for classification task is deduced.
Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm
Medical image segmentation based on deep learning is often faced with the problems of insufficient datasets and long time-consuming labeling.
Ferromagnetic metallic Sr-rich Ln$_{1/2}$A$_{1/2}$CoO$_3$ cobaltites with spontaneous spin rotation
We have studied half-doped Ln$_{0. 50}$(Sr$_{1-x}$A$_x$)$_{0. 50}$CoO$_3$ cobaltites varying the ionic radius rA of A-site cations (divalent cations and lanthanides) in order to complete the T-rA phase diagram.
Materials Science
First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities.
Ranked #9 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Text Level Graph Neural Network for Text Classification
Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information.
Ranked #2 on
Text Classification
on Ohsumed
Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning
Next, the T1, T1$\rho$, T2* relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared. The models developed using ROIs provided by two radiologists achieved high Dice scores of 0. 860 and 0. 833, while the radiologists' manual segmentations achieved a Dice score of 0. 820.
Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation
The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model.
A Neural Question Answering Model Based on Semi-Structured Tables
Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer.
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
We evaluate our approach on two review datasets, Yelp and Amazon.
Ranked #6 on
Unsupervised Text Style Transfer
on Yelp
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce.
Cascading Multiway Attentions for Document-level Sentiment Classification
Document-level sentiment classification aims to assign the user reviews a sentiment polarity.
Ranked #5 on
Sentiment Analysis
on User and product information
Interactive Attention Networks for Aspect-Level Sentiment Classification
In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning.
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
The objective is to find a way to place these items that can minimize the surface area of the bin.
System Combination for Short Utterance Speaker Recognition
This paper presents a combination approach to the SUSR tasks with two phonetic-aware systems: one is the DNN-based i-vector system and the other is our recently proposed subregion-based GMM-UBM system.
Implicit Discourse Relation Classification via Multi-Task Neural Networks
Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser.
