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In image recognition, researchers and developers constantly seek innovative approaches to enhance the accuracy and efficiency of computer vision systems. Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and ... | 1 INTRODUCTION Self-attention-based architectures, in particular Transformers (Vaswani et al., 2017), have become the model of choice in natural language processing (NLP). The dominant approach is to pre-train on a large text corpus and then fine-tune on a smaller task-specific dataset (Devlin et al., 2019). Thanks to Tr... |
Software is quickly replacing what was previously a mechanical realm. These recent technological advances are replacing human expertise to some extent as well, especially when it comes to domains related to image processing and software-driven mobility. In the past, sensors were used to capture clear photos in low ligh... | 1. Preliminaries: mip-NeRF Let us first describe how a fully-trained mip-NeRF [3] renders the color of a single ray cast into the scene r(t) = o+td, where o and d are the origin and direction of the ray respectively, and t denotes distance along the ray. In mip- NeRF, a sorted vector of distances t is defined and the ray... |
The capacity to infer user preferences from past behaviors is crucial for effective personalized suggestions. The fact that many products don’t have star ratings makes this task exponentially more challenging. Past actions are generally interpreted in a binary form to indicate whether or not a user has interacted with ... | 1 Introduction Understanding preferences based on users’ historical activities is key for personalized recommendation. This is particularly challenging when explicit ratings on many items are not available. In this scenario, historical activities are typically viewed as binary, representing whether a user has interacte... |
Recent advancements in deep neural networks have enabled new approaches to address anatomical segmentation. For instance, state-of-the-art performance in the anatomical segmentation of biomedical images has been attained by deep convolutional neural networks (CNNs). Conventional strategies adopt standard encoder-decode... | GAGGION et al.: HYBRIDGNET 1 Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis Nicol´ as Gaggion, Lucas Mansilla, Candelaria Mosquera, Diego H. Milone and Enzo Ferrante Abstract— Anatomical segmentation is a fundamental task in medical... |
The goal of semantic segmentation, a fundamental problem in computer vision, is to classify each pixel in the input image with a certain class. Autonomous driving, medical image processing, computational photography, etc., are just a few real-world contexts where semantic segmentation can be useful. Therefore, there is... | EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
Han Cai1, Junyan Li2, Muyan Hu3, Chuang Gan4, Song Han1
1MIT, 2Zhejiang University, 3Tsinghua University, 4MIT-IBM Watson AI Lab
https://github.com/mit-han-lab/efficientvit
Abstract
High-resolution dense prediction enables many appeal-
ing ... |
"In a remarkable breakthrough, researchers from Google, Carnegie Mellon University, and Bosch Center(...TRUNCATED) | "1 INTRODUCTION Evaluating the robustness of deep learning models to norm bounded adversarial pertur(...TRUNCATED) |
"The number of CT scans performed and the data processing capacity available have grown over the pas(...TRUNCATED) | "1 TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT images Jakob Wasserthal (...TRUNCATED) |
"Imagine your quadruped friend playing outside or your car showcased in an exclusive showroom. Creat(...TRUNCATED) | "in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text pro(...TRUNCATED) |
"In the cutting-edge sphere of machine learning, manipulating and comprehending data within vast, hi(...TRUNCATED) | "1 INTRODUCTION Neural Networks (NN) learn to transform high dimensional data into meaningful repres(...TRUNCATED) |
"In recent times, the zero-shot and few-shot capabilities of Large Language Models (LLMs) have incre(...TRUNCATED) | "1 INTRODUCTION Large language models (LLMs), particularly those with over 100 billion (100B) parame(...TRUNCATED) |
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