Surveys

Automating Infrastructure Surveying: A Framework for Geometric Measurements and Compliance Assessment Using Point Cloud Data

Automation can play a prominent role in improving efficiency, accuracy, and scalability in infrastructure surveying and assessing construction and compliance standards. This paper presents a framework for automation of geometric measurements and compliance assessment using point cloud data. The proposed approach integrates deep learning-based detection and segmentation, in conjunction with geome...

Aesthetics Without Semantics

While it is easy for human observers to judge an image as beautiful or ugly, aesthetic decisions result from a combination of entangled perceptual and cognitive (semantic) factors, making the understanding of aesthetic judgements particularly challenging from a scientific point of view. Furthermore, our research shows a prevailing bias in current databases, which include mostly beautiful images,...

DispBench: Benchmarking Disparity Estimation to Synthetic Corruptions

Deep learning (DL) has surpassed human performance on standard benchmarks, driving its widespread adoption in computer vision tasks. One such task i...

Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations

Recommender systems are essential for delivering personalized content across digital platforms by modeling user preferences and behaviors. Recently,...

ANCHOR-Grid: Authenticating Smart Grid Digital Twins Using Real-World Anchors.

Integrating digital twins (DTs) into smart grid systems within the Internet of Smart Grid Things (IoSGT) ecosystem brings novel opportunities but also...

Real time intelligent garbage monitoring and efficient collection using Yolov8 and Yolov5 deep learning models for environmental sustainability.

Effective waste management is currently one of the most influential factors in enhancing the quality of life. Increased garbage production has been id...

Data Standards in Audiology: A Mixed-Methods Exploration of Community Perspectives and Implementation Considerations

Objective: The purpose of this study was to explore options for data standardisation in audiology and document the global audiology community's curr...

A Large Language Model for Feasible and Diverse Population Synthesis

Generating a synthetic population that is both feasible and diverse is crucial for ensuring the validity of downstream activity schedule simulation ...

From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction

Deep learning-based medical image-to-mesh reconstruction has rapidly evolved, enabling the transformation of medical imaging data into three-dimensi...

Mitigating Image Captioning Hallucinations in Vision-Language Models

Hallucinations in vision-language models (VLMs) hinder reliability and real-world applicability, usually stemming from distribution shifts between p...

An Active Inference perspective on Neurofeedback Training

Neurofeedback training (NFT) aims to teach self-regulation of brain activity through real-time feedback, but suffers from highly variable outcomes a...

Adversarial Attacks in Multimodal Systems: A Practitioner's Survey

The introduction of multimodal models is a huge step forward in Artificial Intelligence. A single model is trained to understand multiple modalities...

An Automated LLM-based Pipeline for Asset-Level Database Creation to Assess Deforestation Impact

The European Union Deforestation Regulation (EUDR) requires companies to prove their products do not contribute to deforestation, creating a critica...

Coverage Biases in High-Resolution Satellite Imagery

Satellite imagery is increasingly used to complement traditional data collection approaches such as surveys and censuses across scientific disciplin...

The Multimodal Paradox: How Added and Missing Modalities Shape Bias and Performance in Multimodal AI

Multimodal learning, which integrates diverse data sources such as images, text, and structured data, has proven superior to unimodal counterparts i...

Gone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Images

Neural compression methods are gaining popularity due to their superior rate-distortion performance over traditional methods, even at extremely low ...

A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law

This survey explores recent advancements in reasoning large language models (LLMs) designed to mimic "slow thinking" - a reasoning process inspired ...

Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities

Recent years have seen remarkable progress in both multimodal understanding models and image generation models. Despite their respective successes, ...

Text to Image Generation and Editing: A Survey

Text-to-image generation (T2I) refers to the text-guided generation of high-quality images. In the past few years, T2I has attracted widespread atte...

Uncertainty Quantification for Machine Learning in Healthcare: A Survey

Uncertainty Quantification (UQ) is pivotal in enhancing the robustness, reliability, and interpretability of Machine Learning (ML) systems for healt...