Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, demanding accurate automated diagnostic systems. While general-domain vision-la...
While Multimodal Large Language Models have achieved human-like performance on many visual and textual reasoning tasks, their proficiency in fine-grained spatia...
Recent breakthroughs in self-supervised Joint-Embedding Predictive Architectures (JEPAs) have established that regularizing Euclidean representations toward iso...
Overview YOLOv6 is a new step in object detection designed for factories, stores, and cameras everywhere. Built by a team focused on speed and reliability, it...
Vision-Language-Action (VLA) models align vision and language with embodied control, but their object referring ability remains limited when relying solely on t...
Artistic style transfer in generative models remains a significant challenge, as existing methods often introduce style only via model fine-tuning, additional a...
Multimodal Large Language Models (MLLMs) combine visual and textual representations to enable rich reasoning capabilities. However, the high computational cost ...
Vocabulary-free fine-grained image recognition aims to distinguish visually similar categories within a meta-class without a fixed, human-defined label set. Exi...
Deep neural networks often exploit shortcuts. These are spurious cues which are associated with output labels in the training data but are unrelated to task sem...
Myth: Computer Vision is only effective for images and not for videos. Reality: Computer Vision can handle both images and videos, thanks to advancements in tem...
Handwritten text recognition and optical character recognition solutions show excellent results with processing data of modern era, but efficiency drops with La...
Introduction Person re-identification re-ID is the task of finding the same individual across different camera views. It has important applications in security...
