Computer Vision
Imagine being able to separate the foreground from the background in your videos with clear, accurate mattes every time. With AI models like MatAnyone, video matting delivers precise alpha mattes
Object detection has undergone tremendous advancements, with models like YOLOv12, YOLOv11, and Darknet-Based YOLOv7 leading the way in real-time detection. While these models perform exceptionally well on general object detection
A comprehensive step-by-step guide on fine-tuning RetinaNet using PyTorch to achieve 79% accuracy on wildlife detection tasks. In this tutorial, we dive deep into RetinaNet’s architecture, explain the benefits of
Real-time object detection has become essential for many practical applications, and the YOLO (You Only Look Once) series by Ultralytics has always been a state-of-the-art model series, providing a robust
Leaf diseases reduce crop yields and impact food security. Finetuning SAM2 helps detect and segment diseased areas using deep learning. With a small dataset, we achieved 74% IoU, making early
3D Gaussian splatting (3DGS) has recently gained recognition as a groundbreaking approach in radiance fields and computer graphics. It stands out as a jack of all trades, addressing challenges that
Apple's DepthPro is quite impressive, producing pixel-perfect, high-resolution metric depth maps with sharp boundaries through monocular depth estimation. It outperforms all of its contenders like Metric3D v2 and DepthAnything in
Image Captioning using ResNet and LSTM bridges vision and language, enabling machines to "see" images and "describe" them in text. This model powers applications like accessibility for visually impaired users,
Molmo VLM is an open-source Vision-Language Model (VLM) showcasing exceptional capabilities in tasks like pointing, counting, VQA, and clock face recognition. Leveraging the meticulously curated PixMo dataset and a well-optimized
3D Gaussian Splatting (3DGS) is redefining the landscape of 3D computer graphics and vision — but here’s a catch: it achieves groundbreaking results without relying on any neural networks, not
Supervised Learning has been dominant for years, but its reliance on labeled data—a costly and time-consuming resource—creates challenges, especially in areas like medical imaging. On the other hand, Unsupervised Learning,
In recent years, the field of 3D from multi-view has become one of the most popular topics in computer vision conferences, with a high number of submitted papers each year.
