CT image denoising and deblurring with deep learning: current status and perspectives

Y Lei, C Niu, J Zhang, G Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
This article reviews the deep learning methods for computed tomography image denoising
and deblurring separately and simultaneously. Then, we discuss promising directions in this …

Prompt learning in computer vision: a survey

Y Lei, J Li, Z Li, Y Cao, H Shan - Frontiers of Information Technology & …, 2024 - Springer
Prompt learning has attracted broad attention in computer vision since the large pre-trained
vision-language models (VLMs) exploded. Based on the close relationship between vision …

Distilling CLIP with Dual Guidance for Learning Discriminative Human Body Shape Representation

F Liu, M Kim, Z Ren, X Liu - … of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Abstract Person Re-Identification (ReID) holds critical importance in computer vision with
pivotal applications in public safety and crime prevention. Traditional ReID methods reliant …

Explainable artificial intelligence (XAI): from inherent explainability to large language models

F Mumuni, A Mumuni - arxiv preprint arxiv:2501.09967, 2025 - arxiv.org
Artificial Intelligence (AI) has continued to achieve tremendous success in recent times.
However, the decision logic of these frameworks is often not transparent, making it difficult …

Explainable Search and Discovery of Visual Cultural Heritage Collections with Multimodal Large Language Models

T Arnold, L Tilton - arxiv preprint arxiv:2411.04663, 2024 - arxiv.org
Many cultural institutions have made large digitized visual collections available online, often
under permissible re-use licences. Creating interfaces for exploring and searching these …

[PDF][PDF] Comparative Explanations for Recommendation: Research Directions

M Varasteh, E McKinnie, A Aird, D Acuña, R Burke - 2024 - ceur-ws.org
Explanations have a long history in recommender systems. Researchers have studied the
different roles explanations can play, the value of explanations for users, and different …

Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model

Y Lei, Z Li, J Zhang, H Shan - The Thirty-eighth Annual Conference on … - openreview.net
The explainability of deep neural networks (DNNs) is critical for trust and reliability in AI
systems. Path-based attribution methods, such as integrated gradients (IG), aim to explain …

[CITATION][C] 计算机视觉中的提示学**: 综述

Y LEI, J LI, Z LI, Y CAO, H SHAN, AY LEI, AJ LI, AZ LI… - Frontiers, 2024