9 citations · 19 across the 15 of their papers we have counts for
8 papers · 1 filter
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…
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…
Re-ttention: Ultra Sparse Visual Generation via Attention Statistical Reshape
Ruichen Chen, Keith G. Mills, Liyao Jiang +2
Diffusion Transformers (DiT) have become the de-facto model for generating high-quality visual content like videos and images. A huge bottleneck is the attention mechanism where co…
FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers
Ruichen Chen, Keith G. Mills, Di Niu
Diffusion Models (DM) have revolutionized the text-to-image visual generation process. However, the large computational cost and model footprint of DMs hinders practical deployment…