2 citations · 3 across the 10 of their papers we have counts for
11 papers
EviSplat: Preserving Multi-View Evidence in 3D Gaussian Splatting for Open-Vocabulary Segmentation
Sungho Moon, Kota Shimomura, Junwoo Park +4
Open-vocabulary 3D scene understanding enables object localization and segmentation from free-form text queries without a fixed category vocabulary. Many recent methods build on 3D…
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…
Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces
Wonhyeok Choi, Kyumin Hwang, Minwoo Choi +4
Self-supervised monocular depth estimation (SSMDE) has gained attention in the field of deep learning as it estimates depth without requiring ground truth depth maps. This approach…
Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining
Wonhyeok Choi, Kyumin Hwang, Wei Peng +2
Self-supervised monocular depth estimation (SSMDE) aims to predict the dense depth map of a monocular image, by learning depth from RGB image sequences, eliminating the need for gr…
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…
Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator
Wonhyeok Choi, Mingyu Shin, Hyukzae Lee +3
Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicle…
Depth-discriminative Metric Learning for Monocular 3D Object Detection
Wonhyeok Choi, Mingyu Shin, Sunghoon Im
Monocular 3D object detection poses a significant challenge due to the lack of depth information in RGB images. Many existing methods strive to enhance the object depth estimation…
Rotation Matters: Generalized Monocular 3D Object Detection for Various Camera Systems
SungHo Moon, JinWoo Bae, SungHoon Im
Research on monocular 3D object detection is being actively studied, and as a result, performance has been steadily improving. However, 3D object detection performance is significa…