Search Results for author:
Found 10 papers, 6 papers with code
Multi-Loss Rebalancing Algorithm for Monocular Depth Estimation
To address these issues, we propose the loss rebalancing algorithm that initializes and rebalances the weight for each loss function adaptively in the course of training.
Versatile Depth Estimator Based on Common Relative Depth Estimation and Camera-Specific Relative-to-Metric Depth Conversion
A typical monocular depth estimator is trained for a single camera, so its performance drops severely on images taken with different cameras.
PNI : Industrial Anomaly Detection using Position and Neighborhood Information
Because anomalous samples cannot be used for training, many anomaly detection and localization methods use pre-trained networks and non-parametric modeling to estimate encoded feature distribution.
Ranked #4 on
Anomaly Detection
on BTAD
(using extra training data)
Depth Map Decomposition for Monocular Depth Estimation
We propose a novel algorithm for monocular depth estimation that decomposes a metric depth map into a normalized depth map and scale features.
Ranked #37 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
RD-Optimized Trit-Plane Coding of Deep Compressed Image Latent Tensors
DPICT is the first learning-based image codec supporting fine granular scalability.
DPICT: Deep Progressive Image Compression Using Trit-Planes
We propose the deep progressive image compression using trit-planes (DPICT) algorithm, which is the first learning-based codec supporting fine granular scalability (FGS).
Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing
We propose a novel loss weighting algorithm, called loss scale balancing (LSB), for multi-task learning (MTL) of pixelwise vision tasks.
Monocular Depth Estimation Using Relative Depth Maps
We propose a novel algorithm for monocular depth estimation using relative depth maps.
Ranked #63 on
Monocular Depth Estimation
on NYU-Depth V2
Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement
We propose a monocular depth estimation algorithm, which extracts a depth map from a single image, based on whole strip masking (WSM) and reliability-based refinement.
Single-Image Depth Estimation Based on Fourier Domain Analysis
We propose a deep learning algorithm for single-image depth estimation based on the Fourier frequency domain analysis.
