6 papers
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…
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…
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…