Search Results for author:
Found 6 papers, 1 papers with code
Meta Variance Transfer: Learning to Augment from the Others
Humans have the ability to robustly recognize objects with various factors of variations such as nonrigid transformation, background noise, and change in lighting conditions.
Quality-Agnostic Image Recognition via Invertible Decoder
Our two-stage scheme allows the network to produce clean-like and robust features from any quality images, by reconstructing their clean images via the invertible decoder.
Ranked #17 on
Domain Generalization
on ImageNet-C
DiscFace: Minimum Discrepancy Learning for Deep Face Recognition
Softmax-based learning methods have shown state-of-the-art performances on large-scale face recognition tasks.
Ranked #1 on
Face Verification
on CALFW
SRFeat: Single Image Super-Resolution with Feature Discrimination
Generative adversarial networks (GANs) have recently been adopted to single image super resolution (SISR) and showed impressive results with realistically synthesized high-frequency textures.
Video Semantic Object Segmentation by Self-Adaptation of DCNN
This paper proposes a new framework for semantic segmentation of objects in videos.
RDFNet: RGB-D Multi-Level Residual Feature Fusion for Indoor Semantic Segmentation
Feature fusion blocks learn residual RGB and depth features and their combinations to fully exploit the complementary characteristics of RGB and depth data.
Ranked #27 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
