4 citations · 14 across the 20 of their papers we have counts for
5 papers · 1 filter
Saddle-Free Guidance: Improved On-Manifold Sampling without Labels or Additional Training
Eric Yeats, Darryl Hannan, Wilson Fearn +3
Score-based generative models require guidance in order to generate plausible, on-manifold samples. The most popular guidance method, Classifier-Free Guidance (CFG), is only applic…
FMG-Det: Foundation Model Guided Robust Object Detection
Darryl Hannan, Timothy Doster, Henry Kvinge +2
Collecting high quality data for object detection tasks is challenging due to the inherent subjectivity in labeling the boundaries of an object. This makes it difficult to not only…
Foundation Models for Remote Sensing: An Analysis of MLLMs for Object Localization
Darryl Hannan, John Cooper, Dylan White +3
Multimodal large language models (MLLMs) have altered the landscape of computer vision, obtaining impressive results across a wide range of tasks, especially in zero-shot settings.…
SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions
Sameera Horawalavithana, Sai Munikoti, Ian Stewart +2
Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent. Despite its popularity, this idea is less explored in improving LLMs to align e…
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Elliott Skomski, Aaron Tuor, Andrew Avila +5
Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images. Despite success on…