Search Results for author:
Found 15 papers, 3 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.
Towards Accurate Facial Landmark Detection via Cascaded Transformers
We formulate facial landmark detection as a coordinate regression task such that the model can be trained end-to-end.
Ranked #2 on
Face Alignment
on COFW
Pushing the Performance Limit of Scene Text Recognizer without Human Annotation
A robust consistency regularization based semi-supervised framework is proposed for STR, which can effectively solve the instability issue due to domain inconsistency between synthetic and real images.
Self-Supervised Dense Consistency Regularization for Image-to-Image Translation
Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs).
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation
We encode all panoptic entities in a video, including both foreground instances and background semantics, with a unified representation called panoptic slots.
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
Controllable Image Restoration for Under-Display Camera in Smartphones
However, this causes inevitable image degradation in the form of spatially variant blur and noise because of the opaque display in front of the camera.
RaScaNet: Learning Tiny Models by Raster-Scanning Images
RaScaNet reads only a few rows of pixels at a time using a convolutional neural network and then sequentially learns the representation of the whole image using a recurrent neural network.
Self-Reorganizing and Rejuvenating CNNs for Increasing Model Capacity Utilization
In this paper, we propose self-reorganizing and rejuvenating convolutional neural networks; a biologically inspired method for improving the computational resource utilization of neural networks.
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm Under Mixed Illumination
Images in our dataset are mostly captured with illuminants existing in the scene, and the ground truth illumination is computed by taking the difference between the images with different illumination combination.
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
A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone
For the second issue, we define a new metric to measure the robustness of gaze estimator, and propose an adversarial training based Disturbance with Ordinal loss (DwO) method to improve it.
Deep Hierarchical-Hyperspherical Learning (DH^2L)
To generate evenly distributed parameters, we constrain them to lie on \emph{hierarchical hyperspheres}.
Fast adversarial training for semi-supervised learning
The proposed method generates bad samples of high-quality by use of the adversarial training used in VAT.
Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy.
