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cs.CV2024

Efficient Few-Shot Neural Architecture Search by Counting the Number of Nonlinear Functions

Youngmin Oh, Hyunju Lee, Bumsub Ham

Neural architecture search (NAS) enables finding the best-performing architecture from a search space automatically. Most NAS methods exploit an over-parameterized network (i.e., a…

cs.CV2024

Cerberus: Attribute-based person re-identification using semantic IDs

Chanho Eom, Geon Lee, Kyunghwan Cho +3

We introduce a new framework, dubbed Cerberus, for attribute-based person re-identification (reID). Our approach leverages person attribute labels to learn local and global person…

cs.CV2024

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…

cs.CV2024

Toward INT4 Fixed-Point Training via Exploring Quantization Error for Gradients

Dohyung Kim, Junghyup Lee, Jeimin Jeon +2

Network quantization generally converts full-precision weights and/or activations into low-bit fixed-point values in order to accelerate an inference process. Recent approaches to…

cs.CV2024

FYI: Flip Your Images for Dataset Distillation

Byunggwan Son, Youngmin Oh, Donghyeon Baek +1

Dataset distillation synthesizes a small set of images from a large-scale real dataset such that synthetic and real images share similar behavioral properties (e.g, distributions o…