Search Results for author:
Found 15 papers, 8 papers with code
Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning
In pursuit of a more scalable 3D reconstruction, we propose a federated learning framework with 3DGS, which is a decentralized framework and can potentially use distributed computational resources across millions of clients.
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields
Therefore, to address these problems, we propose a federated learning pipeline for large-scale modeling with NeRF.
Feature Space Particle Inference for Neural Network Ensembles
Ensembles of deep neural networks demonstrate improved performance over single models.
HCFormer: Unified Image Segmentation with Hierarchical Clustering
Hierarchical clustering is an effective and efficient approach widely used for classical image segmentation methods.
TeachAugment: Data Augmentation Optimization Using Teacher Knowledge
Optimization of image transformation functions for the purpose of data augmentation has been intensively studied.
Implicit Integration of Superpixel Segmentation into Fully Convolutional Networks
Superpixels are a useful representation to reduce the complexity of image data.
Irregularly Tabulated MLP for Fast Point Feature Embedding
Aiming at drastic speedup for point-feature embeddings at test time, we propose a new framework that uses a pair of multi-layer perceptrons (MLP) and a lookup table (LUT) to transform point-coordinate inputs into high-dimensional features.
Rethinking PointNet Embedding for Faster and Compact Model
PointNet, which is the widely used point-wise embedding method and known as a universal approximator for continuous set functions, can process one million points per second.
Superpixel Segmentation via Convolutional Neural Networks with Regularized Information Maximization
We propose an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time.
Tabulated MLP for Fast Point Feature Embedding
Aiming at a drastic speedup for point-data embeddings at test time, we propose a new framework that uses a pair of multi-layer perceptron (MLP) and look-up table (LUT) to transform point-coordinate inputs into high-dimensional features.
Adversarial Transformations for Semi-Supervised Learning
We propose a Regularization framework based on Adversarial Transformations (RAT) for semi-supervised learning.
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely.
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper. challenge.
Changing Fashion Cultures
The paper presents a novel concept that analyzes and visualizes worldwide fashion trends.
Semantic Change Detection with Hypermaps
By the detected change areas, however, a human cannot understand how different the two images.
