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Found 17 papers, 8 papers with code
MULTI: Multimodal Understanding Leaderboard with Text and Images
Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short context.
Online Transfer Learning for RSV Case Detection
Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications.
A Survey of Heterogeneous Transfer Learning
These methods typically presuppose identical feature spaces and label spaces in both domains, known as homogeneous transfer learning, which, however, is not always a practical assumption.
Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning
Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge.
Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion
The MoLE performs specialized learning of multi-modal local features, prompting the fused images to retain the local information in a sample-adaptive manner, while the MoGE focuses on the global information that complements the fused image with overall texture detail and contrast.
DetFusion: A Detection-driven Infrared and Visible Image Fusion Network
We cascade the image fusion network with the detection networks of both modalities and use the detection loss of the fused images to provide guidance on task-related information for the optimization of the image fusion network.
Online Active Regression
Active regression considers a linear regression problem where the learner receives a large number of data points but can only observe a small number of labels.
Single Pass Entrywise-Transformed Low Rank Approximation
A natural way to do this would be to simply apply $f$ to each entry of $A$, and then compute the matrix decomposition, but this requires storing all of $A$ as well as multiple passes over its entries.
Local Aggressive Adversarial Attacks on 3D Point Cloud
However, the perturbations of global point are not effective for misleading the victim model.
Tensor Random Projection for Low Memory Dimension Reduction
The TRP map is formed as the Khatri-Rao product of several smaller random projections, and is compatible with any base random projection including sparse maps, which enable dimension reduction with very low query cost and no floating point operations.
Multi-Drone based Single Object Tracking with Agent Sharing Network
Moreover, an agent sharing network (ASNet) is proposed by self-supervised template sharing and view-aware fusion of the target from multiple drones, which can improve the tracking accuracy significantly compared with single drone tracking.
Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
To address this dilemma, we further propose an uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework to extract complementary information from cross-modal images, which can significantly improve the detection performance in low light conditions.
FrequentNet: A Novel Interpretable Deep Learning Model for Image Classification
This paper has proposed a new baseline deep learning model of more benefits for image classification.
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations
Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms.
Low-Rank Tucker Approximation of a Tensor From Streaming Data
This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor.
Large Spectral Density Matrix Estimation by Thresholding
Spectral density matrix estimation of multivariate time series is a classical problem in time series and signal processing.
Kernel Distillation for Fast Gaussian Processes Prediction
Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature.
