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Found 22 papers, 19 papers with code
HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese
We investigate the usage of entity linking (EL)in downstream tasks and present the first modularized EL toolkit for easy task adaptation.
MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification
Change detection (CD) from remote sensing (RS) images using deep learning has been widely investigated in the literature.
Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking
Detecting non-factual content is a longstanding goal to increase the trustworthiness of large language models (LLMs) generations.
Frequency Decomposition-Driven Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation
Cross-domain semantic segmentation of remote sensing (RS) imagery based on unsupervised domain adaptation (UDA) techniques has significantly advanced deep-learning applications in the geosciences.
RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentation
Experimental results on two widely used datasets, ISPRS Vaihingen and LoveDA Urban, demonstrate the effectiveness and potential of the proposed RS3Mamba.
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
We introduce TableLLM, a robust large language model (LLM) with 13 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios.
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
However, the high-frequency details generated by DDPM often suffer from misalignment with HR images due to the model's tendency to overlook long-range semantic contexts.
Reverse That Number! Decoding Order Matters in Arithmetic Learning
Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations.
CodeS: Towards Building Open-source Language Models for Text-to-SQL
To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task.
Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery
The presence of cloud layers severely compromises the quality and effectiveness of optical remote sensing (RS) images.
SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
Furthermore, the boundary loss capitalizes on the distinctive features of SGB by directing the model's attention to the boundary information of the object.
GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM
Furthermore, the Difference Conditional Encoder (DCE), is designed to guide the generation of CD maps by exploiting multi-level difference features.
GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image Superresolution
To cope with this problem, this work proposes a novel global context-driven residual dense network (GCRDN) for satellite image SR based on the encoder and decoder architecture.
GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation
We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge.
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning.
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions.
Gated SwitchGAN for multi-domain facial image translation
The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation.
Graph Contrastive Learning for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing
Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there still lacks of control over the generation process in order to achieve semantic face editing.
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the models transferability in an adversarial manner.
A Comparative Analysis of Feature Selection Methods for Biomarker Discovery in Study of Toxicant-treated Atlantic Cod (Gadus morhua) Liver
Univariate and multivariate feature selection methods can be used for biomarker discovery in analysis of toxicant exposure.
EFSIS: Ensemble Feature Selection Integrating Stability
There are basically two strategies for ensemble feature selection, namely data perturbation and function perturbation.
