1 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
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…
A Generalization of Continuous Relaxation in Structured Pruning
Brad Larson, Bishal Upadhyaya, Luke McDermott +1
Deep learning harnesses massive parallel floating-point processing to train and evaluate large neural networks. Trends indicate that deeper and larger neural networks with an incre…
Distilled Pruning: Using Synthetic Data to Win the Lottery
Luke McDermott, Daniel Cummings
This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic…