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Found 38 papers, 15 papers with code
Task-oriented Domain-specific Meta-Embedding for Text Classification
Meta-embedding learning, which combines complementary information in different word embeddings, have shown superior performances across different Natural Language Processing tasks.
Out-of-Distribution Generalization in Time Series: A Survey
Time series frequently manifest distribution shifts, diverse latent features, and non-stationary learning dynamics, particularly in open and evolving environments.
Out-of-Distribution Generalization
Representation Learning
+2
Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction
Based on our new StepEval-Audio-360 evaluation benchmark, Step-Audio achieves state-of-the-art performance in human evaluations, especially in terms of instruction following.
DULRTC-RME: A Deep Unrolled Low-rank Tensor Completion Network for Radio Map Estimation
Radio maps enrich radio propagation and spectrum occupancy information, which provides fundamental support for the operation and optimization of wireless communication systems.
DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
Distributed DNN inference is becoming increasingly important as the demand for intelligent services at the network edge grows.
Baichuan-Omni-1.5 Technical Report
We introduce Baichuan-Omni-1. 5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities.
Med-R$^2$: Crafting Trustworthy LLM Physicians through Retrieval and Reasoning of Evidence-Based Medicine
In recent years, Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios.
VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs
Despite their potential, existing work mainly focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains.
RemoteDet-Mamba: A Hybrid Mamba-CNN Network for Multi-modal Object Detection in Remote Sensing Images
Unmanned aerial vehicle (UAV) remote sensing is widely applied in fields such as emergency response, owing to its advantages of rapid information acquisition and low cost.
Baichuan-Omni Technical Report
The salient multimodal capabilities and interactive experience of GPT-4o highlight its critical role in practical applications, yet it lacks a high-performing open-source counterpart.
Ranked #20 on
Visual Question Answering
on MM-Vet
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions.
Multimodal generative semantic communication based on latent diffusion model
At the receiving end, the framework can reconstruct the original multimodal images based on the semantic maps.
V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems
Cooperative LiDAR systems integrating vehicles and road infrastructure, termed V2I calibration, exhibit substantial potential, yet their deployment encounters numerous challenges.
Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events
To this end, we first construct two multimodal dense and occlusion vehicle detection datasets for large-scale events, utilizing RGB and height map modalities.
Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning
In this paper, we propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I).
DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality
Specifically, DeepAdaIn-Net encompasses a partition point selection (PPS) module, a high feature compression learning (HFCL) module, a bandwidth-aware feature configuration (BaFC) module, and a feature consistency compensation (FCC) module.
UIU-Net: U-Net in U-Net for Infrared Small Object Detection
RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information.
SC-wLS: Towards Interpretable Feed-forward Camera Re-localization
Visual re-localization aims to recover camera poses in a known environment, which is vital for applications like robotics or augmented reality.
MPANet: Multi-Patch Attention For Infrared Small Target object Detection
To this end, a multi-patch attention network (MPANet) based on the axial-attention encoder and the multi-scale patch branch (MSPB) structure is proposed.
Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis
The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer.
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification
The resulting \textit{attention-fused hybrid network} (AfNet) is based on three attention-fused parallel hybrid sub-nets with different kernels in each block repeatedly using high-level features to enhance the final ground-truth maps.
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor.
Deep Learning for UAV-based Object Detection and Tracking: A Survey
Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS).
Classification of Diabetic Retinopathy Severity in Fundus Images with DenseNet121 and ResNet50
In this work, deep learning algorithms are used to classify fundus images in terms of diabetic retinopathy severity.
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation
Our method enables fast adaptation of deep VO networks to unseen environments in a self-supervised manner.
Gamma-ray Astrophysics in the MeV Range: the ASTROGAM Concept and Beyond
The energy range between about 100 keV and 1 GeV is of interest for a vast class of astrophysical topics.
Instrumentation and Methods for Astrophysics
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.
General Classification
Hyperspectral Image Classification
+1
TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process
Neural response generative models have achieved remarkable progress in recent years but tend to yield irrelevant and uninformative responses.
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.
Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network
In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site.
User Behavior Retrieval for Click-Through Rate Prediction
These retrieved behaviors are then fed into a deep model to make the final prediction instead of simply using the most recent ones.
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
In this paper, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs).
Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment
A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL).
Efficient Method for Categorize Animals in the Wild
Thanks to advanced regularization strategies and ensemble learning, we got top 7/336 places in the final leaderboard.
Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection
To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB).
Predict Future Sales using Ensembled Random Forests
This is a method report for the Kaggle data competition 'Predict future sales'.
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features
To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy.
In-Orbit Instrument Performance Study and Calibration for POLAR Polarization Measurements
POLAR is a compact space-borne detector designed to perform reliable measurements of the polarization for transient sources like Gamma-Ray Bursts in the energy range 50-500keV.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Instrumentation and Detectors
