Product Alert

Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation

Sarcasm is a linguistic phenomenon that intends to ridicule a target (e.g., entity, event, or person) in an inherent way. Multimodal Sarcasm Explanation (MuSE) aims at revealing the intended irony in a sarcastic post using a natural language explanation. Though important, existing systems overlooked the significance of the target of sarcasm in generating explanations. In this paper, we propose a...

Dense Object Detection Based on De-homogenized Queries

Dense object detection is widely used in automatic driving, video surveillance, and other fields. This paper focuses on the challenging task of dense object detection. Currently, detection methods based on greedy algorithms, such as non-maximum suppression (NMS), often produce many repetitive predictions or missed detections in dense scenarios, which is a common problem faced by NMS-based algori...

CS-SHAP: Extending SHAP to Cyclic-Spectral Domain for Better Interpretability of Intelligent Fault Diagnosis

Neural networks (NNs), with their powerful nonlinear mapping and end-to-end capabilities, are widely applied in mechanical intelligent fault diagnos...

HODDI: A Dataset of High-Order Drug-Drug Interactions for Computational Pharmacovigilance

Drug-side effect research is vital for understanding adverse reactions arising in complex multi-drug therapies. However, the scarcity of higher-orde...

DCENWCNet: A Deep CNN Ensemble Network for White Blood Cell Classification with LIME-Based Explainability

White blood cells (WBC) are important parts of our immune system, and they protect our body against infections by eliminating viruses, bacteria, par...

AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers

Post-training quantization (PTQ) has emerged as a promising solution for reducing the storage and computational cost of vision transformers (ViTs). ...

xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods

The growing complexity of machine learning and deep learning models has led to an increased reliance on opaque "black box" systems, making it diffic...

RGB-Event ISP: The Dataset and Benchmark

Event-guided imaging has received significant attention due to its potential to revolutionize instant imaging systems. However, the prior methods pr...

Post-Training Quantization for 3D Medical Image Segmentation: A Practical Study on Real Inference Engines

Quantizing deep neural networks ,reducing the precision (bit-width) of their computations, can remarkably decrease memory usage and accelerate proce...

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters

There has been significant focus on creating neuro-symbolic models for interpretable image classification using Convolutional Neural Networks (CNNs)...

Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback

Reinforcement learning (RL) has demonstrated success in automating insulin dosing in simulated type 1 diabetes (T1D) patients but is currently unabl...

A Post-Processing-Based Fair Federated Learning Framework

Federated Learning (FL) allows collaborative model training among distributed parties without pooling local datasets at a central server. However, t...

Distributed Conformal Prediction via Message Passing

Post-hoc calibration of pre-trained models is critical for ensuring reliable inference, especially in safety-critical domains such as healthcare. Co...

Post-hoc Spurious Correlation Neutralization with Single-Weight Fictitious Class Unlearning

Neural network training tends to exploit the simplest features as shortcuts to greedily minimize training loss. However, some of these features migh...

Multimodal Sensor Dataset for Monitoring Older Adults Post Lower-Limb Fractures in Community Settings

Lower-Limb Fractures (LLF) are a major health concern for older adults, often leading to reduced mobility and prolonged recovery, potentially impair...

Correctness Assessment of Code Generated by Large Language Models Using Internal Representations

Ensuring the correctness of code generated by Large Language Models (LLMs) presents a significant challenge in AI-driven software development. Exist...

Latent-space adversarial training with post-aware calibration for defending large language models against jailbreak attacks

Ensuring safety alignment has become a critical requirement for large language models (LLMs), particularly given their widespread deployment in real...

Classifier Ensemble for Efficient Uncertainty Calibration of Deep Neural Networks for Image Classification

This paper investigates novel classifier ensemble techniques for uncertainty calibration applied to various deep neural networks for image classific...

Coded Deep Learning: Framework and Algorithm

The success of deep learning (DL) is often achieved with large models and high complexity during both training and post-training inferences, hinderi...

Deep learning for temporal super-resolution 4D Flow MRI

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, appar...