297 citations · 366 across the 9 of their papers we have counts for
10 papers
FlowTool: Controlling Tool Parameter in Image Retouching via Flow Matching
Thanh-Long V. Le, Steven Walton, Seunghyun Yoon +4
Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially generate reasoning, tool selection…
Smaller, Faster, Cheaper: Architectural Designs for Efficient Machine Learning
Steven Walton
Major advancements in the capabilities of computer vision models have been primarily fueled by rapid expansion of datasets, model parameters, and computational budgets, leading to…
Distilling Normalizing Flows
Steven Walton, Valeriy Klyukin, Maksim Artemev +3
Explicit density learners are becoming an increasingly popular technique for generative models because of their ability to better model probability distributions. They have advanta…
Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light
Ali Hassani, Fengzhe Zhou, Aditya Kane +13
Many sparse attention mechanisms such as Neighborhood Attention have typically failed to consistently deliver speedup over the self attention baseline. This is largely due to the l…
Efficient Image Generation with Variadic Attention Heads
Steven Walton, Ali Hassani, Xingqian Xu +2
While the integration of transformers in vision models have yielded significant improvements on vision tasks they still require significant amounts of computation for both training…
Design Amortization for Bayesian Optimal Experimental Design
Noble Kennamer, Steven Walton, Alexander Ihler
Bayesian optimal experimental design is a sub-field of statistics focused on developing methods to make efficient use of experimental resources. Any potential design is evaluated i…
Neighborhood Attention Transformer
Ali Hassani, Steven Walton, Jiachen Li +2
We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision. NA is a pixel-wise operation, localizing self attention (SA)…
SeMask: Semantically Masked Transformers for Semantic Segmentation
Jitesh Jain, Anukriti Singh, Nikita Orlov +4
Finetuning a pretrained backbone in the encoder part of an image transformer network has been the traditional approach for the semantic segmentation task. However, such an approach…
ConvMLP: Hierarchical Convolutional MLPs for Vision
Jiachen Li, Ali Hassani, Steven Walton +1
MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transfo…
Escaping the Big Data Paradigm with Compact Transformers
Ali Hassani, Steven Walton, Nikhil Shah +3
With the rise of Transformers as the standard for language processing, and their advancements in computer vision, there has been a corresponding growth in parameter size and amount…