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Found 13 papers, 9 papers with code
Hyperspectral Image Denoising via Self-Modulating Convolutional Neural Networks
At the core of the model lies a novel block, which we call spectral self-modulating residual block (SSMRB), that allows the network to transform the features in an adaptive manner based on the adjacent spectral data, enhancing the network's ability to handle complex noise.
Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency Transfer
To address these challenges, we propose a saliency-guided approach that leverages attention information to improve the performance of LiDAR odometry estimation and semantic segmentation models.
Spherical Vision Transformer for 360-degree Video Saliency Prediction
The growing interest in omnidirectional videos (ODVs) that capture the full field-of-view (FOV) has gained 360-degree saliency prediction importance in computer vision.
ST360IQ: No-Reference Omnidirectional Image Quality Assessment with Spherical Vision Transformers
As their popularity has increased dramatically in recent years, evaluating the quality of 360 images has become a problem of interest since it provides insights for capturing, transmitting, and consuming this new media.
Exploring Object-Aware Attention Guided Frame Association for RGB-D SLAM
This can be very useful for visual tasks such as simultaneous localization and mapping (SLAM) where CNN representations of spatially attentive object locations may lead to improved performance.
SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks
We first use the proposed feedback model to learn saliency distribution from pseudo-ground-truth.
When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition
The second stage maps these features into high level representations with a fully randomized structure of recursive neural networks (RNNs) efficiently.
Ranked #2 on
Scene Recognition
on SUN-RGBD
Salient object detection on hyperspectral images using features learned from unsupervised segmentation task
Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes.
An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness
Bottom-up and top-down visual cues are two types of information that helps the visual saliency models.
Hyperspectral Image Dataset for Benchmarking on Salient Object Detection
Many works have been done on salient object detection using supervised or unsupervised approaches on colour images.
Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion
To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data.
Saliency Detection by Forward and Backward Cues in Deep-CNNs
As prior knowledge of objects or object features helps us make relations for similar objects on attentional tasks, pre-trained deep convolutional neural networks (CNNs) can be used to detect salient objects on images regardless of the object class is in the network knowledge or not.
Saliency Fusion in Eigenvector Space with Multi-Channel Pulse Coupled Neural Network
Saliency computation has become a popular research field for many applications due to the useful information provided by saliency maps.
