4 citations · 8 across the 7 of their papers we have counts for
8 papers · 1 filter
Disentangled Representations for Short-Term and Long-Term Person Re-Identification
Chanho Eom, Wonkyung Lee, Geon Lee +1
We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is…
Instance-Aware Group Quantization for Vision Transformers
Jaehyeon Moon, Dohyung Kim, Junyong Cheon +1
Post-training quantization (PTQ) is an efficient model compression technique that quantizes a pretrained full-precision model using only a small calibration set of unlabeled sample…
AZ-NAS: Assembling Zero-Cost Proxies for Network Architecture Search
Junghyup Lee, Bumsub Ham
Training-free network architecture search (NAS) aims to discover high-performing networks with zero-cost proxies, capturing network characteristics related to the final performance…
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration
Hyekang Park, Jongyoun Noh, Youngmin Oh +2
We address the problem of network calibration adjusting miscalibrated confidences of deep neural networks. Many approaches to network calibration adopt a regularization-based metho…
RankMixup: Ranking-Based Mixup Training for Network Calibration
Jongyoun Noh, Hyekang Park, Junghyup Lee +1
Network calibration aims to accurately estimate the level of confidences, which is particularly important for employing deep neural networks in real-world systems. Recent approache…
Camera-Driven Representation Learning for Unsupervised Domain Adaptive Person Re-identification
Geon Lee, Sanghoon Lee, Dohyung Kim +3
We present a novel unsupervised domain adaption method for person re-identification (reID) that generalizes a model trained on a labeled source domain to an unlabeled target domain…