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Found 8 papers, 5 papers with code
Multi-Scale Estimation for Omni-Directional Saliency Maps Using Learnable Equator Bias
This paper proposes a novel saliency-map estimation model for the omni-directional images by extracting overlapping 2-dimensional (2D) plane images from omni-directional images at various directions and angles of view.
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixer
First, since a convolutional layer only processes a local area, it is difficult to propagate the information of an input snapshot picture embedded in the center of the omni-directional image to the edges of the image.
Improving NeRF with Height Data for Utilization of GIS Data
Neural Radiance Fields (NeRF) has been applied to various tasks related to representations of 3D scenes.
Improving Translation Invariance in Convolutional Neural Networks with Peripheral Prediction Padding
Zero padding is often used in convolutional neural networks to prevent the feature map size from decreasing with each layer.
Omni-Directional Image Generation from Single Snapshot Image
For these purposes, a novel computer vision task to generate ODI from a single snapshot image is proposed in this paper.
LPM: Learnable Pooling Module for Efficient Full-Face Gaze Estimation
This study tackles this problem through compression of the input full-face image by removing redundant information using a novel learnable pooling module.
Influence of Image Classification Accuracy on Saliency Map Estimation
However, there is no research on the relationship between the image classification accuracy and the performance of the saliency map estimation.
Saliency Map Estimation for Omni-Directional Image Considering Prior Distributions
In recent years, the deep learning techniques have been applied to the estimation of saliency maps, which represent probability density functions of fixations when people look at the images.
