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Found 38 papers, 19 papers with code
SpectralMamba: Efficient Mamba for Hyperspectral Image Classification
Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences.
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation
To address these issues, existing approaches use additional semantic priori black-box networks to separate moving objects and improve the model only at the loss level.
RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs).
On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models
Big models have achieved revolutionary breakthroughs in the field of AI, but they might also pose potential concerns.
Aligning Language Models for Versatile Text-based Item Retrieval
Our empirical studies demonstrate that fine-tuning embedding models on the dataset leads to remarkable improvements in a variety of retrieval tasks.
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection
These factors also limit the performance of the well-known low-rank representation (LRR) models in terms of robustness on the separation of background and target features and the reliance on manual parameter selection.
RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability
Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training.
Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations
In this paper, we propose a general paradigm that augments LLMs with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE.
Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values
The rapid advancement of Large Language Models (LLMs) has attracted much attention to value alignment for their responsible development.
SpectralGPT: Spectral Remote Sensing Foundation Model
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
Unpacking the Ethical Value Alignment in Big Models
Big models have greatly advanced AI's ability to understand, generate, and manipulate information and content, enabling numerous applications.
Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e. g., single cities or regions).
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
In this paper, we bridge the gap between recommender models and LLMs, combining their respective strengths to create a versatile and interactive recommender system.
From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
Big models, exemplified by Large Language Models (LLMs), are models typically pre-trained on massive data and comprised of enormous parameters, which not only obtain significantly improved performance across diverse tasks but also present emergent capabilities absent in smaller models.
Interpretable End-to-End Driving Model for Implicit Scene Understanding
Driving scene understanding is to obtain comprehensive scene information through the sensor data and provide a basis for downstream tasks, which is indispensable for the safety of self-driving vehicles.
Towards Explainable Collaborative Filtering with Taste Clusters Learning
In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN.
CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection
In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost.
Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval
Based on comprehensive experiments on popular retrieval benchmarks, we verify that clusters and terms indeed complement each other, enabling HI$^2$ to achieve lossless retrieval quality with competitive efficiency across various index settings.
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
In order to unify these two stages, we explore a model-based indexer for document retrieval.
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface at a distance of data acquisition devices, such as aircraft, spacecraft, and satellite.
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images.
Hyperspectral Image Super-Resolution
Image Super-Resolution
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Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way.
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index
Web search provides a promising way for people to obtain information and has been extensively studied.
UA-FedRec: Untargeted Attack on Federated News Recommendation
Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue.
AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images
Extensive experimental results on a publicly available dataset from Myocardial pathology segmentation combining multi-sequence CMR (MyoPS 2020) demonstrate our method can achieve promising performance compared with other state-of-the-art methods.
Learning to Select Historical News Articles for Interaction based Neural News Recommendation
The key to personalized news recommendation is to match the user's interests with the candidate news precisely and efficiently.
USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
Search and recommendation are the two most common approaches used by people to obtain information.
SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies.
Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing
Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneously generalize various spectral variabilities and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various spectral variabilities.
Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model
Moreover, to better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i. e., Houston2013 -- hyperspectral and multispectral data, Berlin -- hyperspectral and synthetic aperture radar (SAR) data, Augsburg -- hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification.
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS).
Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field
Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications.
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
In particular, we also investigate a special case of multi-modality learning (MML) -- cross-modality learning (CML) that exists widely in RS image classification applications.
Graph Convolutional Networks for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral feature representations.
Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning
However, the ability in the fusion of HS and MS images remains to be improved, particularly in large-scale scenes, due to the limited acquisition of HS images.
Spatial-Spectral Manifold Embedding of Hyperspectral Data
In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and remote sensing community.
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR).
