Search Results for author:
Found 11 papers, 3 papers with code
Learning Object Placement by Inpainting for Compositional Data Augmentation
We study the problem of common sense placement of visual objects in an image.
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation
A major obstacle to the development of effective monocular depth estimation algorithms is the difficulty in obtaining high-quality depth data that corresponds to collected RGB images.
Learning Scene Context Without Images
Teaching machines of scene contextual knowledge would enable them to interact more effectively with the environment and to anticipate or predict objects that may not be immediately apparent in their perceptual field.
DIFAI: Diverse Facial Inpainting using StyleGAN Inversion
Image inpainting is an old problem in computer vision that restores occluded regions and completes damaged images.
Reference Guided Image Inpainting using Facial Attributes
Image inpainting is a technique of completing missing pixels such as occluded region restoration, distracting objects removal, and facial completion.
Controllable Face Manipulation and UV Map Generation by Self-supervised Learning
Our model relies on pretrained StyleGAN, and the proposed model is trained in a self-supervised manner without any manual annotations or datasets.
Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis
To alleviate the issue, many 3D-aware GANs have been proposed and shown notable results, but 3D GANs struggle with editing semantic attributes.
Generate and Edit Your Own Character in a Canonical View
Although the progress of generative models enables the stylization of a portrait, obtaining the stylized image in canonical view is still a challenging task.
CPNet: Cross-Parallel Network for Efficient Anomaly Detection
Anomaly detection in video streams is a challenging problem because of the scarcity of abnormal events and the difficulty of accurately annotating them.
A Statistician Teaches Deep Learning
Deep learning (DL) has gained much attention and become increasingly popular in modern data science.
DLA: Compiler and FPGA Overlay for Neural Network Inference Acceleration
Overlays have shown significant promise for field-programmable gate-arrays (FPGAs) as they allow for fast development cycles and remove many of the challenges of the traditional FPGA hardware design flow.
Distributed, Parallel, and Cluster Computing Hardware Architecture Signal Processing
