A study on visual language models explores how shared semantic frameworks improve image–text understanding across ...

The study, titled “Teach AI What It Doesn’t Know,” published in AI Magazine, presents a detailed research agenda by Sean Du of Nanyang Technological University, focused on building reliable machine ...

ABSTRACT: Image dehazing is an ill-posed low-level computer vision problem that substantially affects the reliability of outdoor vision systems, including autonomous driving, intelligent surveillance, ...

ABSTRACT: Image dehazing is an ill-posed low-level computer vision problem that substantially affects the reliability of outdoor vision systems, including autonomous driving, intelligent surveillance, ...

Abstract: Recent research works showed that deep neural networks are vulnerable to adversarial examples, which are usually maliciously created by carefully adding deliberate and imperceptible ...

IFAP generates adversarial perturbations using model gradients and then shapes them in the discrete cosine transform (DCT) domain. Unlike existing frequency-aware methods that apply a fixed frequency ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

This repository contains the implementation of topological data analysis (TDA) methods for detecting adversarial examples in deep learning models, particularly focusing on Vision-Language models like ...

Today, Apple confirmed its participation in the 2025 International Conference on Computer Vision (ICCV), which will take place from October 19 to 23 in Honolulu. Here are the studies the company will ...

Computer vision moved fast in 2025: new multimodal backbones, larger open datasets, and tighter model–systems integration. Practitioners need sources that publish rigorously, link code and benchmarks, ...