4 citations · 7 across the 9 of their papers we have counts for
10 papers
RAIN: Robotic Task Generalization with Region-Aware Interaction Networks
Seunghun Lee, Kyuhwan Shim, Yeogyeong Song +5
Existing vision-language-action (VLA) models benefit from large-scale vision-language pretraining, but tend to memorize training trajectories, limiting their ability to adapt learn…
CVA: Context-aware Video-text Alignment for Video Temporal Grounding
Sungho Moon, Seunghun Lee, Jiwan Seo +1
We propose Context-aware Video-text Alignment (CVA), a novel framework to address a significant challenge in video temporal grounding: achieving temporally sensitive video-text ali…
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…
ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation
Seunghun Lee, Wonhyeok Choi, Changjae Kim +2
In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretra…
DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation
Seunghun Lee, Sunghyun Cho, Sunghoon Im
In this paper, we present DRANet, a network architecture that disentangles image representations and transfers the visual attributes in a latent space for unsupervised cross-domain…