1 citations · 2 across the 3 of their papers we have counts for
5 papers
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models
Dan Jacobellis, Daniel Cummings, Neeraja J. Yadwadkar
In the field of neural data compression, the prevailing focus has been on optimizing algorithms for either classical distortion metrics, such as PSNR or SSIM, or human perceptual q…
Neural Architecture Codesign for Fast Bragg Peak Analysis
Luke McDermott, Jason Weitz, Dmitri Demler +3
We develop an automated pipeline to streamline neural architecture codesign for fast, real-time Bragg peak analysis in high-energy diffraction microscopy. Traditional approaches, n…
UniCat: Crafting a Stronger Fusion Baseline for Multimodal Re-Identification
Jennifer Crawford, Haoli Yin, Luke McDermott +1
Multimodal Re-Identification (ReID) is a popular retrieval task that aims to re-identify objects across diverse data streams, prompting many researchers to integrate multiple modal…
Linear Mode Connectivity in Sparse Neural Networks
Luke McDermott, Daniel Cummings
With the rise in interest of sparse neural networks, we study how neural network pruning with synthetic data leads to sparse networks with unique training properties. We find that…
GraFT: Gradual Fusion Transformer for Multimodal Re-Identification
Haoli Yin, Jiayao Li, Eva Schiller +2
Object Re-Identification (ReID) is pivotal in computer vision, witnessing an escalating demand for adept multimodal representation learning. Current models, although promising, rev…