9 papers
Efficient Neural Network Model Selection for Few-Class Application Datasets
Bryan Bo Cao, Abhinav Sharma, Lawrence O'Gorman +2
While much effort has focused on developing and benchmarking high-performance neural networks, less attention has been given to how dataset properties, known to practitioners, can…
FeudalNav: A Simple Framework for Visual Navigation
Faith Johnson, Bryan Bo Cao, Shubham Jain +2
Visual navigation for robotics is inspired by the human ability to navigate environments using visual cues and memory, eliminating the need for detailed maps. In unseen, unmapped,…
ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning
Nicholas Meegan, Hansi Liu, Bryan Bo Cao +5
We introduce ViFiCon, a self-supervised contrastive scheme which learns a cross-modal association between vision and wireless modalities. Specifically, the system uses pedestrian d…
YOPO-Nav: Visual Navigation using 3DGS Graphs from One-Pass Videos
Ryan Meegan, Adam D'Souza, Bryan Bo Cao +2
Visual navigation has emerged as a practical alternative to traditional robotic navigation pipelines that rely on detailed mapping and path planning. However, constructing and main…
StatsMerging: Statistics-Guided Model Merging via Task-Specific Teacher Distillation
Ranjith Merugu, Bryan Bo Cao, Shubham Jain
Model merging has emerged as a promising solution to accommodate multiple large models within constrained memory budgets. We present StatsMerging, a novel lightweight learning-base…
Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement
Bryan Bo Cao, Lawrence O'Gorman, Michael Coss +1
We propose Few-Class Arena (FCA), as a unified benchmark with focus on testing efficient image classification models for few classes. A wide variety of benchmark datasets with many…