Search Results for author:
Found 11 papers, 2 papers with code
Integrated In-vehicle Monitoring System Using 3D Human Pose Estimation and Seat Belt Segmentation
In addition, we propose a classification task to distinguish between normal and abnormal states of wearing a seat belt using results that combine 3D pose estimation with seat-belt segmentation.
AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning
Finally, the coordinate channel, which represents the pixel location information, is concatenated to an input of AnoSeg to consider the positional relationship of each pixel in the image.
Ranked #57 on
Anomaly Detection
on MVTec AD
Selective Focusing Learning in Conditional GANs
Conditional generative adversarial networks (cGANs) have demonstrated remarkable success due to their class-wise controllability and superior quality for complex generation tasks.
Core-set Sampling for Efficient Neural Architecture Search
Neural architecture search (NAS), an important branch of automatic machine learning, has become an effective approach to automate the design of deep learning models.
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation
By applying the attention map to an image feature map, ADGAN learns the normal class distribution from which the useless region is removed, and it is possible to greatly reduce the problem difficulty of the anomaly detection task.
Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning
The bidirectional boundary region rearrangement enables the generation of the missing region using bidirectional information similar to that of the image inpainting task, thereby generating the higher quality than the conventional methods using unidirectional information.
End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Images
We tackle the problem in stack reconstruction-based methods by proposing a novel framework with a fully differentiable high dynamic range imaging (HDRI) process.
An Efficient Accelerator Design Methodology for Deformable Convolutional Networks
In this paper, we present a novel approach to accelerate deformable convolution on FPGA.
Towards Design Methodology of Efficient Fast Algorithms for Accelerating Generative Adversarial Networks on FPGAs
To implement the DeConv layer in hardware, the state-of-the-art accelerator reduces the high computational complexity via the DeConv-to-Conv conversion and achieves the same results.
Deep Recursive HDRI: Inverse Tone Mapping using Generative Adversarial Networks
Because most images have a low dynamic range, recovering the lost dynamic range from a single low dynamic range image is still prevalent.
Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image
The proposed model is based on a convolutional neural network composed of dilated convolutional layers, and infers LDR images with various exposures and illumination from a single LDR image of the same scene.
