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Found 14 papers, 2 papers with code
Diffusart: Enhancing Line Art Colorization with Conditional Diffusion Models
Colorization of line art drawings is an important task in illustration and animation workflows.
How to estimate carbon footprint when training deep learning models? A guide and review
Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society.
A patch-based architecture for multi-label classification from single label annotations
In this paper, we propose a patch-based architecture for multi-label classification problems where only a single positive label is observed in images of the dataset.
Analysis of Different Losses for Deep Learning Image Colorization
While learning to automatically colorize an image, one can define well-suited objective functions related to the desired color output.
Influence of Color Spaces for Deep Learning Image Colorization
In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the question: "Is it crucial to correctly choose the right color space in deep-learning based colorization?".
Unraveling the Hidden Environmental Impacts of AI Solutions for Environment
In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and in the first place greenhouse gas emissions (GHG).
3D Object Detection and Pose Estimation of Unseen Objects in Color Images with Local Surface Embeddings
We demonstrate the performance of this approach on the T-LESS dataset, by using a small number of objects to learn the embedding and testing it on the other objects.
Multi-task deep learning for image segmentation using recursive approximation tasks
The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features.
Variational Osmosis for Non-linear Image Fusion
We propose a new variational model for non-linear image fusion.
A multi-task U-net for segmentation with lazy labels
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation.
LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net
We propose LU-Net -- for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud.
Learning to segment microscopy images with lazy labels
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud
This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud.
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches
Superpixels have become very popular in many computer vision applications.
