Search Results for author:
Found 18 papers, 7 papers with code
MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.
Consistency-based Semi-supervised Learning for Object detection
Making a precise annotation in a large dataset is crucial to the performance of object detection.
Ranked #18 on
Semi-Supervised Object Detection
on COCO 2% labeled data
Interpolation-based semi-supervised learning for object detection
Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much.
Class-Imbalanced Semi-Supervised Learning
First, we analyze existing SSL methods in imbalanced environments and examine how the class imbalance affects SSL methods.
Residual Features and Unified Prediction Network for Single Stage Detection
They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances.
Superpixel-based Semantic Segmentation Trained by Statistical Process Control
Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks.
Enhancement of SSD by concatenating feature maps for object detection
In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD.
Selective Self-Training for semi-supervised Learning
While the conventional methods cannot be applied to the new SSL problems where the separated data do not share the classes, our method does not show any performance degradation even if the classes of unlabeled data are different from those of the labeled data.
Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction
In the source domain, we fully train an object detector and the RRPN with full supervision of HOI.
Deep Learning-based High-precision Depth Map Estimation from Missing Viewpoints for 360 Degree Digital Holography
In this paper, we propose a novel, convolutional neural network model to extract highly precise depth maps from missing viewpoints, especially well applicable to generate holographic 3D contents.
Imposing Consistency for Optical Flow Estimation
Imposing consistency through proxy tasks has been shown to enhance data-driven learning and enable self-supervision in various tasks.
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling
We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames.
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow
Also, we present a novel dynamic coarse-to-fine cost volume processing during various stages of refinement to avoid multiple levels of cost volumes.
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation.
Ranked #11 on
Monocular Depth Estimation
on KITTI Eigen split
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
Ranked #1 on
Monocular Depth Estimation
on KITTI Eigen split
OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation
We propose OCAI, a method that supports robust frame interpolation by generating intermediate video frames alongside optical flows in between.
SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations
Optical flow estimation is crucial to a variety of vision tasks.
