9 citations · 37 across the 20 of their papers we have counts for
6 papers · 1 filter
Domain Adaptation of SAM 2.1 for Building-Component Segmentation in Image-Based 3D Reconstruction
Ehsan Agha Ebrahimi, Yong-Cheol Lee, Keith G. Mills
3D reconstruction of building components using image-based scanning requires appropriate segmentation of the objects in the 2D building images. In order to benefit from consistent…
Segmentation of Bovid Dentition Under Imperfect Annotations: A Comparative Study of Convolutional and Attention Models
Keith G. Mills, Evan B. Sanders, Gregory J. Matthews +1
Semantic segmentation decomposes an image into distinct mask regions corresponding to different object categories, such as people, cars, signs or buildings. Advances in machine lea…
How Much Does Message Passing Matter? A Drop-In Study of GNN Layers for Neural Network Graph Regression
Keith G. Mills, Aedan J. DeFrates, Joong Ho Kim
Graph Neural Networks (GNNs) are widely used as regressors, for example to predict the accuracy of a neural architecture. Yet new message-passing (MP) layers are developed and benc…
Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So?
Joong Ho Kim, Keith G. Mills
Diffusion Models (DM) have revolutionized text-driven generation by enabling the synthesis of high-quality, photorealistic visual content from user prompts. Whereas prior advances…
2D Pre-Training for 3D Pose Estimation
Liyao Jiang, Ruichen Chen, Keith G. Mills
Pre-training is a general method that is used in a range of deep learning tasks. By first training a model on one task, and then further training on the downstream task used for fi…
Naïve PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation
Joong Ho Kim, Nicholas Thai, Souhardya Saha Dip +2
Text-to-Image (T2I) generation is primarily driven by Diffusion Models (DM) which rely on random Gaussian noise. Thus, like playing the slots at a casino, a DM will produce differe…