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Found 8 papers, 0 papers with code
CBARF: Cascaded Bundle-Adjusting Neural Radiance Fields from Imperfect Camera Poses
Existing volumetric neural rendering techniques, such as Neural Radiance Fields (NeRF), face limitations in synthesizing high-quality novel views when the camera poses of input images are imperfect.
Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing
First, we adopt the particle swarm optimization (PSO) algorithm to speed up the search of a discriminant dimension that is expressed as a linear combination of current dimensions.
GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences
Supervised and unsupervised deep trackers that rely on deep learning technologies are popular in recent years.
Design of Supervision-Scalable Learning Systems: Methodology and Performance Benchmarking
The design of robust learning systems that offer stable performance under a wide range of supervision degrees is investigated in this work.
Subspace Learning Machine (SLM): Methodology and Performance
The partitioning process is recursively applied at each child node to build an SLM tree.
On Supervised Feature Selection from High Dimensional Feature Spaces
The application of machine learning to image and video data often yields a high dimensional feature space.
Unsupervised Lightweight Single Object Tracking with UHP-SOT++
Based on the experimental results, we compare pros and cons of supervised and unsupervised trackers and provide a new perspective to understand the performance gap between supervised and unsupervised methods, which is the third contribution of this work.
UHP-SOT: An Unsupervised High-Performance Single Object Tracker
An unsupervised online object tracking method that exploits both foreground and background correlations is proposed and named UHP-SOT (Unsupervised High-Performance Single Object Tracker) in this work.
