4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.CV2025
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model
Navin Ranjan, Andreas Savakis
The Segment Anything Model (SAM) is a popular vision foundation model; however, its high computational and memory demands make deployment on resource-constrained devices challengin…
cs.CV2024
Waterfall Transformer for Multi-person Pose Estimation
Navin Ranjan, Bruno Artacho, Andreas Savakis
We propose the Waterfall Transformer architecture for Pose estimation (WTPose), a single-pass, end-to-end trainable framework designed for multi-person pose estimation. Our framewo…
cs.CV2024★ 4 cited
LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation
Navin Ranjan, Andreas Savakis
Vision transformers (ViTs) have demonstrated remarkable performance across various visual tasks. However, ViT models suffer from substantial computational and memory requirements,…