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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

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Showing 2019Show all

7 papers · 1 filter

cs.CV20193 cited

Relation Network for Person Re-identification

Hyunjong Park, Bumsub Ham

Person re-identification (reID) aims at retrieving an image of the person of interest from a set of images typically captured by multiple cameras. Recent reID methods have shown th…

cs.CV2019

Learning Semantic Correspondence Exploiting an Object-level Prior

Junghyup Lee, Dohyung Kim, Wonkyung Lee +2

We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We pr…

cs.CV201936 cited

Learning Disentangled Representation for Robust Person Re-identification

Chanho Eom, Bumsub Ham

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.CV2019

Deformable Kernel Networks for Joint Image Filtering

Beomjun Kim, Jean Ponce, Bumsub Ham

Joint image filters are used to transfer structural details from a guidance picture used as a prior to a target image, in tasks such as enhancing spatial resolution and suppressing…

cs.CV2019

Temporally Consistent Depth Prediction with Flow-Guided Memory Units

Chanho Eom, Hyunjong Park, Bumsub Ham

Predicting depth from a monocular video sequence is an important task for autonomous driving. Although it has advanced considerably in the past few years, recent methods based on c…

cs.CV2019

SFNet: Learning Object-aware Semantic Correspondence

Junghyup Lee, Dohyung Kim, Jean Ponce +1

We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We pr…