Developing effective path representations has become increasingly essential across various fields within intelligent transportation. Although pre-trained path representation learning models have shown improved performance, they predominantly focus on the topological structures from single modality data, i.e., road networks, overlooking the geometric and contextual features associated with path-related images, e.g., remote sensing images. Similar to human understanding, integrating information from multiple modalities can provide a more comprehensive view, enhancing both representation accuracy and generalization. However, variations in information granularity impede the semantic alignment of road network-based paths (road paths) and image-based paths (image paths), while the heterogeneity of multi-modal data poses substantial challenges for effective fusion and utilization. In this paper, we propose a novel Multi-modal, Multi-granularity Path Representation Learning Framework (MM-Path), which can learn a generic path representation by integrating modalities from both road paths and image paths. To enhance the alignment of multi-modal data, we develop a multi-granularity alignment strategy that systematically associates nodes, road sub-paths, and road paths with their corresponding image patches, ensuring the synchronization of both detailed local information and broader global contexts. To address the heterogeneity of multi-modal data effectively, we introduce a graph-based cross-modal residual fusion component designed to comprehensively fuse information across different modalities and granularities. Finally, we conduct extensive experiments on two large-scale real-world datasets under two downstream tasks, validating the effectiveness of the proposed MM-Path. The code is available at: https://github.com/decisionintelligence/MM-Path.

While neural networks have made significant strides in many AI tasks, they remain vulnerable to a range of noise types, including natural corruptions, adversarial noise, and low-resolution artifacts. Many existing approaches focus on enhancing robustness against specific noise types, limiting their adaptability to others. Previous studies have addressed general robustness by adopting a spectral perspective, which tends to blur crucial features like texture and object contours. Our proposed solution, however, introduces an inverse scale variational sparsification framework within a time-continuous inverse scale space formulation. This framework progressively learns finer-scale features by discerning variational differences between pixels, ultimately preserving only large-scale features in the smoothed image. Unlike frequency-based methods, our approach not only removes noise by smoothing small-scale features where corruptions often occur but also retains high-contrast details such as textures and object contours. Moreover, our framework offers simplicity and efficiency in implementation. By integrating this algorithm into neural network training, we guide the model to prioritize learning large-scale features. We show the efficacy of our approach through enhanced robustness against various noise types.

Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.

Running your Python scripts is an important step in the development process, because it is in this manner that you'll get to find out if your code works as you intended it to. It is, also, often the case that we would need to pass information to the Python script for it to function. In this tutorial, you will discover various ways of running and passing information to a Python script. Running and Passing Information to a Python Script Photo by Andrea Leopardi, some rights reserved. The command-line interface is used extensively for running Python code.

Hello folks:) This is my final year research project based on deep learning. Let me give an introduction about my project first. When we talk about banana it's a famous fruit that commonly available across the world, because it instantly boosts your energy. Bananas are one most consumed fruit in the world. According to modern calculations, Bananas are grown in around 107 countries since it makes a difference to lower blood pressure and to reduce the chance of cancer and asthma.

Recently me and my team shifted our approach to build our models in the docker containers rather than creating and running a python file on the system. For me, it was a completely new experience in learning docker. If you are also quite new to docker then with this post you will be able to create your own basic deep-learning model in docker. Docker provides an image-based deployment model. This makes it easy to share an application, with all of their dependencies across multiple environments.

In this tutorial, you will learn how to use multiprocessing with OpenCV and Python to perform feature extraction. You'll learn how to use multiprocessing with OpenCV to parallelize feature extraction across the system bus, including all processors and cores on your computer. Today's tutorial is inspired by PyImageSearch reader, Abigail. Hey Adrian, I just read your tutorial on image hashing with OpenCV and really enjoyed it. I'm trying to apply image hashing to my research project at the university.

Today we are going to discuss a fundamental developer, engineer, and computer scientist skill -- command line arguments. Command line arguments are an elementary skill that you must learn how to use, especially if you are trying to apply more advanced computer vision, image processing, or deep learning concepts. If you are new to command line arguments or do not know how to use them that's okay! But you still need to take the time to educate yourself on how to use them -- this post will help you do exactly that. By the end of today's post you will have a strong understanding of command line arguments, how they work, and how to use them. Each day I receive 3-5 emails or comments from PyImageSearch readers who are struggling with command line arguments.