39 citations · 83 across the 9 of their papers we have counts for
10 papers
TF-PRVR: Training-Free Partially Relevant Video Retrieval
Giyeol Kim, Chanho Eom
Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos containing moments relevant to a given text query. Despite recent progress, existing PRVR methods suffer…
FAST-GOAL: Fast and Efficient Global-local Object Alignment Learning
Hyungyu Choi, Young Kyun Jang, Chanho Eom
Vision-language models such as CLIP have shown impressive capabilities in aligning images and text, but they often struggle with lengthy and detailed text descriptions due to pre-t…
DiCo: Disentangled Concept Representation for Text-to-image Person Re-identification
Giyeol Kim, Chanho Eom
Text-to-image person re-identification (TIReID) aims to retrieve person images from a large gallery given free-form textual descriptions. TIReID is challenging due to the substanti…
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search
Jeimin Jeon, Youngmin Oh, Junghyup Lee +4
N-shot neural architecture search (NAS) exploits a supernet containing all candidate subnets for a given search space. The subnets are typically trained with a static training stra…
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…
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…
Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation
Geon Lee, Chanho Eom, Wonkyung Lee +2
We present a novel unsupervised domain adaptation method for semantic segmentation that generalizes a model trained with source images and corresponding ground-truth labels to a ta…
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…
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…
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…