Surveys

Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy

Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions with human expertise. This study focuses on integrating a high-performing image-based emotion model into a HITL annotation framework to evaluate the collaborative potential of human-machine interaction and identify the p...

Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos

With the rapid development of AI-generated content (AIGC), the creation of high-quality AI-generated videos has become faster and easier, resulting in the Internet being flooded with all kinds of video content. However, the impact of these videos on the content ecosystem remains largely unexplored. Video information retrieval remains a fundamental approach for accessing video content. Building o...

Deep Learning in Automated Power Line Inspection: A Review

In recent years, power line maintenance has seen a paradigm shift by moving towards computer vision-powered automated inspection. The utilization of...

CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for Zero-Shot Customized Video Diffusion Transformers

Customized generation has achieved significant progress in image synthesis, yet personalized video generation remains challenging due to temporal in...

From "I have nothing to hide" to "It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data

Location data collection has become widespread with smart phones becoming ubiquitous. Smart phone apps often collect precise location data from user...

Building a cancer risk and survival prediction model based on social determinants of health combined with machine learning: A NHANES 1999 to 2018 retrospective cohort study.

The occurrence and progression of cancer is a significant focus of research worldwide, often accompanied by a prolonged disease course. Concurrently, ...

FairT2I: Mitigating Social Bias in Text-to-Image Generation via Large Language Model-Assisted Detection and Attribute Rebalancing

The proliferation of Text-to-Image (T2I) models has revolutionized content creation, providing powerful tools for diverse applications ranging from ...

On Fairness of Unified Multimodal Large Language Model for Image Generation

Unified multimodal large language models (U-MLLMs) have demonstrated impressive performance in visual understanding and generation in an end-to-end ...

What is in a name? Mitigating Name Bias in Text Embeddings via Anonymization

Text-embedding models often exhibit biases arising from the data on which they are trained. In this paper, we examine a hitherto unexplored bias in ...

Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.

Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorit...

Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration

Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capabilities but remain highly susceptible to object hallucination, whe...

Efficient Diffusion Models: A Survey

Diffusion models have emerged as powerful generative models capable of producing high-quality contents such as images, videos, and audio, demonstrat...

Safety at Scale: A Comprehensive Survey of Large Model Safety

The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has re...

MIND: Microstructure INverse Design with Generative Hybrid Neural Representation

The inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties. While traditiona...

Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation

Feature selection is an important but challenging task in causal inference for obtaining unbiased estimates of causal quantities. Properly selected ...

Do Audio-Visual Segmentation Models Truly Segment Sounding Objects?

Unlike traditional visual segmentation, audio-visual segmentation (AVS) requires the model not only to identify and segment objects but also to dete...

Validity and Inter-Device Reliability of an Artificial Intelligence App for Real-Time Assessment of 505 Change of Direction Tests.

The present study aimed to explore the validity and inter-device reliability of a novel artificial intelligence app (Asstrapp) for real-time measureme...

ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition

Bias in machine learning models can lead to unfair decision making, and while it has been well-studied in the image and text domains, it remains und...

Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey

The rapid adoption of deep learning in sensitive domains has brought tremendous benefits. However, this widespread adoption has also given rise to s...

Machine Learning Fairness for Depression Detection using EEG Data

This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We ...