Human pose estimation is one of the most critical tasks in computer vision. It aims to localize anatomical key points (like shoulders, knees, and wrists) on the human body. Traditional convolutional neural networks (CNNs) have long dominated this field, but a new horizon has emerged with the advent of transformers Continue Reading
Computer Vision
YOLO: From Real-Time to State-of-the-Art Object Detection
The You Only Look Once (YOLO) series has revolutionized object detection since its inception in 2015. Developed initially by Joseph Redmon and colleagues, YOLO redefined speed and efficiency in computer vision by transforming detection into a single regression problem. Unlike earlier two-stage detectors (e.g., R-CNN), which required multiple passes over Continue Reading
GradCAM and its Implementation in PyTorch
Deep learning models, especially convolutional neural networks (CNNs), often function as black boxes, making it difficult to interpret their decision-making processes. Gradient-weighted Class Activation Mapping (GradCAM) is a powerful technique used to visualize and understand these models by highlighting the regions of an image that contribute most to a prediction. Continue Reading
Visual Question Answering from Scratch using TensorFlow
Visual Question Answering (VQA) is a fascinating field in artificial intelligence where a system answers questions about an image. This combines natural language processing (NLP) to understand the question and computer vision to analyze the image. For example, given an image of a red apple and the question “What color Continue Reading
[Paper Summary] EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
![[Paper Summary] EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation](https://idiotdeveloper.com/wp-content/uploads/2024/09/Paper-Summary-EMCAD-Efficient-Multi-scale-Convolutional-Attention-Decoding-for-Medical-Image-Segmentation-1024x536.jpg)
This post will analyze the research paper “EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation.” We will discuss the problems with existing medical image segmentation methods and how the given method (EMCAD) solves these issues. What is EMCAD? EMCAD is a newly developed efficient multi-scale convolutional attention decoder Continue Reading
What is Image Captioning?
In recent years, the field of artificial intelligence (AI) has seen remarkable advancements, particularly in how machines can understand and describe visual content. One of the fascinating developments in this area is image captioning, where AI models are trained to generate descriptive captions for images. This technology, often referred to Continue Reading
Image Masking with OpenCV AddWeighted
Image masking is a powerful technique used in image processing to manipulate specific parts of an image while leaving other areas untouched. This is particularly useful in applications like object detection, image segmentation, and photo editing. In this tutorial, we’ll explore how to perform image masking using OpenCV addWeighted function. Continue Reading
ResUNet++ Implementation in TensorFlow
In this article, we will study the ResUNet++ architecture and implement it using the TensorFlow framework. ResUNet++ is a medical image segmentation architecture built upon the ResUNet architecture. It takes advantage of Residual Networks, Squeeze and Excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP), and attention blocks. What is ResUNet++? Debesh Continue Reading
UNet 3+ Implementation in TensorFlow
In this article, we will implement the UNet 3+ architecture using TensorFlow. UNet 3+ extends the classic UNet and UNet++ architecture incorporating full skip connections. We will delve into each block of the UNet 3+ architecture, explaining how they work and how they contribute to improving the model’s performance. Understanding these Continue Reading
Skip Connection in Image Segmentation: UNet, UNet++ and UNet 3+
Image segmentation, a fundamental task in computer vision, involves partitioning an image into multiple segments to simplify its representation. One of the critical advancements in image segmentation architectures is the integration of skip connections, which have revolutionized the field by improving the accuracy and efficiency of segmentation models. What are Continue Reading