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Found 16 papers, 5 papers with code
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.
P2Net: A Post-Processing Network for Refining Semantic Segmentation of LiDAR Point Cloud based on Consistency of Consecutive Frames
To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method.
Surgical Skill Assessment via Video Semantic Aggregation
Automated video-based assessment of surgical skills is a promising task in assisting young surgical trainees, especially in poor-resource areas.
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
SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera
This paper presents a novel semantic-based online extrinsic calibration approach, SOIC (so, I see), for Light Detection and Ranging (LiDAR) and camera sensors.
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.
Rare Event Detection using Disentangled Representation Learning
The effectiveness of the proposed approach is verified by the quantitative evaluations on four change detection datasets, and the qualitative analysis shows that the proposed method can acquire the representations that disentangle rare events from trivial ones.
Scale Estimation of Monocular SfM for a Multi-modal Stereo Camera
This paper proposes a novel method of estimating the absolute scale of monocular SfM for a multi-modal stereo camera.
Object Detection in Satellite Imagery using 2-Step Convolutional Neural Networks
This paper presents an efficient object detection method from satellite imagery.
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.
Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints
This paper presents a novel method for detecting scene changes from a pair of images with a difference of camera viewpoints using a dense optical flow based change detection network.
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs.
Ranked #8 on
Cloud Removal
on SEN12MS-CR
Object Detection of Satellite Images Using Multi-Channel Higher-order Local Autocorrelation
The Earth observation satellites have been monitoring the earth's surface for a long time, and the images taken by the satellites contain large amounts of valuable data.
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.
