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Found 11 papers, 3 papers with code
Surface Geometry Processing: An Efficient Normal-based Detail Representation
With the rapid development of high-resolution 3D vision applications, the traditional way of manipulating surface detail requires considerable memory and computing time.
A Lightweight Recurrent Learning Network for Sustainable Compressed Sensing
While the initial reconstruction sub-network has a hierarchical structure to progressively recover the image, reducing the number of parameters, the residual reconstruction sub-network facilitates recurrent residual feature extraction via recurrent learning to perform both feature fusion and deep reconstructions across different scales.
URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement
Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement.
Adversarial Learning for Zero-shot Domain Adaptation
With the hypothesis that the shift between a given pair of domains is shared across tasks, we propose a new method for ZSDA by transferring domain shift from an irrelevant task (IrT) to the task of interest (ToI).
Spectral Analysis Network for Deep Representation Learning and Image Clustering
This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis, which is a popular technique with solid theory foundations.
Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
To train CoCoGAN in the absence of target-domain data for RT, we propose a new supervisory signal, i. e. the alignment between representations across tasks.
SA-Net: A deep spectral analysis network for image clustering
In this paper, we propose a deep spectral analysis network for unsupervised representation learning and image clustering.
An unsupervised deep learning framework via integrated optimization of representation learning and GMM-based modeling
In comparison with the existing work in similar areas, our objective function has two learning targets, which are created to be jointly optimized to achieve the best possible unsupervised learning and knowledge discovery from unlabeled data sets.
A Simple Pooling-Based Design for Real-Time Salient Object Detection
We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway.
Ranked #1 on
RGB Salient Object Detection
on SOD
GeoCapsNet: Aerial to Ground view Image Geo-localization using Capsule Network
The task of cross-view image geo-localization aims to determine the geo-location (GPS coordinates) of a query ground-view image by matching it with the GPS-tagged aerial (satellite) images in a reference dataset.
SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection
To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance.
