4 citations · 7 across the 9 of their papers we have counts for
8 papers · 1 filter
Temporal Grounding as a Learning Signal for Referring Video Object Segmentation
Seunghun Lee, Jiwan Seo, Jeonghoon Kim +9
Referring Video Object Segmentation (RVOS) aims to segment and track objects in videos based on natural language expressions, requiring precise alignment between visual content and…
Latest Object Memory Management for Temporally Consistent Video Instance Segmentation
Seunghun Lee, Jiwan Seo, Minwoo Choi +6
In this paper, we present Latest Object Memory Management (LOMM) for temporally consistent video instance segmentation that significantly improves long-term instance tracking. At t…
Bridging Geometric and Semantic Foundation Models for Generalized Monocular Depth Estimation
Sanggyun Ma, Wonjoon Choi, Jihun Park +4
We present Bridging Geometric and Semantic (BriGeS), an effective method that fuses geometric and semantic information within foundation models to enhance Monocular Depth Estimatio…
Style-Editor: Text-driven object-centric style editing
Jihun Park, Jongmin Gim, Kyoungmin Lee +2
We present Text-driven object-centric style editing model named Style-Editor, a novel method that guides style editing at an object-centric level using textual inputs. The core of…
CAVIS: Context-Aware Video Instance Segmentation
Seunghun Lee, Jiwan Seo, Kiljoon Han +2
In this paper, we introduce the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information…
Offline-to-Online Knowledge Distillation for Video Instance Segmentation
Hojin Kim, Seunghun Lee, Sunghoon Im
In this paper, we present offline-to-online knowledge distillation (OOKD) for video instance segmentation (VIS), which transfers a wealth of video knowledge from an offline model t…