← Search

Devin Guillory

7 accepted papers

2024

Hyperbolic Active Learning for Semantic Segmentation under Domain Shift

ICML 2024poster

We introduce a hyperbolic neural network approach to pixel-level active learning for semantic segmentation. Analysis of the data statistics leads to a novel interpretation of the hyperbolic radius as an indicator of data scarcity. In HALO (Hyperbolic Active Learning Optimization), for the first time…

2023

Using Language to Extend to Unseen Domains

ICLR 2023top-25%

It is expensive to collect training data for every possible domain that a vision model may encounter when deployed. We instead consider how simply $\textit{verbalizing}$ the training domain (e.g.``photos of birds'') as well as domains we want to extend to but do not have data for (e.g.``paintings of…

2022

Disentangled Action Recognition with Knowledge Bases

NAACL 2022long

Action in video usually involves the interaction of human with objects. Action labels are typically composed of various combinations of verbs and nouns, but we may not have training data for all possible combinations. In this paper, we aim to improve the generalization ability of the compositional a…

2022

Studying Bias in GANs through the Lens of Race

ECCV 2022poster

"In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of the datasets upon which these models are trained. By examining and controlling the racial distributions in various training datasets, we are able to observe the impacts of…

Cited by 50SourcePDFScholar
2021

Predicting With Confidence on Unseen Distributions

ICCV 2021poster

Recent work has shown that the accuracy of machine learning models can vary substantially when evaluated on a distribution that even slightly differs from that of the training data. As a result, predicting model performance on previously unseen distributions without access to labeled data is an impo…

Cited by 164PDFScholar
2020

Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning

ECCV 2020poster

Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments). Since only the b…

2016

A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues

RSS 2016poster

In order to track dynamic objects in a robot’s environment, one must first segment the scene into a collection of separate objects. Most real-time robotic vision systems today rely on simple spatial relations to segment the scene into separate objects. However, such methods fail under a variety of…

Cited by 54SourcePDFScholar