Search Results for author:
Found 16 papers, 4 papers with code
Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion
We propose a high-quality 3D-to-3D conversion method, Instruct 3D-to-3D.
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter
We address the challenge of training a large supernet for the object detection task, using a relatively small amount of training data.
NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights, and Materials of Real Object
The goal of inverse rendering is to decompose geometry, lights, and materials given pose multi-view images.
Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Models
Our goal is to develop fine-grained real-image editing methods suitable for real-world applications.
Thinking the Fusion Strategy of Multi-reference Face Reenactment
In recent advances of deep generative models, face reenactment -manipulating and controlling human face, including their head movement-has drawn much attention for its wide range of applicability.
Data Cleansing for Deep Neural Networks with Storage-efficient Approximation of Influence Functions
Moreover, our simple and general proposed method to calculate influence scores is available on our auto ML tool without programing, Neural Network Console.
Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision
Transformer architectures have brought about fundamental changes to computational linguistic field, which had been dominated by recurrent neural networks for many years.
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools.
Reference-Based Video Colorization with Spatiotemporal Correspondence
To address this issue, we warp colors only from the regions on the reference frame restricted by correspondence in time.
Out-of-core Training for Extremely Large-Scale Neural Networks With Adaptive Window-Based Scheduling
We propose a novel out-of-core algorithm that enables faster training of extremely large-scale neural networks with sizes larger than allotted GPU memory.
Fully Convolutional Search Heuristic Learning for Rapid Path Planners
Path-planning algorithms are an important part of a wide variety of robotic applications, such as mobile robot navigation and robot arm manipulation.
Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding
Spectral embedding provides a framework for solving perceptual organization problems, including image segmentation and figure/ground organization.
Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression
We demonstrate results on both the synthetic images of Sintel and the real images of the classic MIT intrinsic image dataset.
Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
We consider the visual sentiment task of mapping an image to an adjective noun pair (ANP) such as "cute baby".
Learning Lightness From Human Judgement on Relative Reflectance
We develop a new approach to inferring lightness, the perceived reflectance of surfaces, from a single image.
