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Found 13 papers, 4 papers with code
You Only Look at Once for Real-time and Generic Multi-Task
In this study, we present an adaptive, real-time, and lightweight multi-task model designed to concurrently address object detection, drivable area segmentation, and lane line segmentation tasks.
Ranked #3 on
Lane Detection
on BDD100K val
An Attentive-based Generative Model for Medical Image Synthesis
This study proposes an attention-based dual contrast generative model, called ADC-cycleGAN, which can synthesize medical images from unpaired data with multiple slices.
An Ensemble Semi-Supervised Adaptive Resonance Theory Model with Explanation Capability for Pattern Classification
Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of prototype nodes using unlabeled samples.
Auto-Focus Contrastive Learning for Image Manipulation Detection
However, we argue that those models achieve sub-optimal detection performance as it tends to: 1) distinguish the manipulation traces from a lot of noisy information within the entire image, and 2) ignore the trace relations among the pixels of each manipulated region and its surroundings.
DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data
Specifically, a dual contrast loss is introduced into the discriminators to indirectly build constraints between real source and synthetic images by taking advantage of samples from the source domain as negative samples and enforce the synthetic images to fall far away from the source domain.
Learning Hierarchical Graph Representation for Image Manipulation Detection
The objective of image manipulation detection is to identify and locate the manipulated regions in the images.
Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection
In this case, the Wasserstein distance can be calculated with the closed-form, even the prior distribution is not Gaussian.
A Review of Generalized Zero-Shot Learning Methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning.
Non-iterative recomputation of dense layers for performance improvement of DCNN
An iterative method of learning has become a paradigm for training deep convolutional neural networks (DCNN).
A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets
Following the rapidly growing digital image usage, automatic image categorization has become preeminent research area.
High Frequency Content based Stimulus for Perceptual Sharpness Assessment in Natural Images
A blind approach to evaluate the perceptual sharpness present in a natural image is proposed.
Full-reference image quality assessment by combining global and local distortion measures
Therefore, we hypothesize that the resulting objective score for an image can be derived from the combination of local and global distortion measures calculated from the reference and test images.
Pulling back error to the hidden-node parameter technology: Single-hidden-layer feedforward network without output weight
SLFNs are universal approximators when at least the parameters of the networks including hidden-node parameter and output weight are exist.
