activity
20162026
most citedDisentangled Representations for Short-Term and Long-Term Person Re-Identification

39 citations · 159 across the 37 of their papers we have counts for

collaborators
Showing 2021Show all

7 papers · 1 filter

cs.CV2021

Video-based Person Re-identification with Spatial and Temporal Memory Networks

Chanho Eom, Geon Lee, Junghyup Lee +1

Video-based person re-identification (reID) aims to retrieve person videos with the same identity as a query person across multiple cameras. Spatial and temporal distractors in per…

cs.CV2021

Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences

Hyunjong Park, Sanghoon Lee, Junghyup Lee +1

We address the problem of visible-infrared person re-identification (VI-reID), that is, retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal…

cs.CV20211 cited

Distance-aware Quantization

Dohyung kim, Junghyup Lee, Bumsub Ham

We address the problem of network quantization, that is, reducing bit-widths of weights and/or activations to lighten network architectures. Quantization methods use a rounding fun…

cs.CV20212 cited

Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation

Donghyeon Baek, Youngmin Oh, Bumsub Ham

We address the problem of generalized zero-shot semantic segmentation (GZS3) predicting pixel-wise semantic labels for seen and unseen classes. Most GZS3 methods adopt a generative…

cs.CV20215 cited

Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation

Youngmin Oh, Beomjun Kim, Bumsub Ham

We address the problem of weakly-supervised semantic segmentation (WSSS) using bounding box annotations. Although object bounding boxes are good indicators to segment corresponding…

cs.CV20218 cited

Network Quantization with Element-wise Gradient Scaling

Junghyup Lee, Dohyung Kim, Bumsub Ham

Network quantization aims at reducing bit-widths of weights and/or activations, particularly important for implementing deep neural networks with limited hardware resources. Most m…