6 papers · 1 filter
Fine-Grained Image-Text Correspondence with Cost Aggregation for Open-Vocabulary Part Segmentation
Jiho Choi, Seonho Lee, Minhyun Lee +2
Open-Vocabulary Part Segmentation (OVPS) is an emerging field for recognizing fine-grained parts in unseen categories. We identify two primary challenges in OVPS: (1) the difficult…
MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
Minhyun Lee, Seungho Lee, Song Park +3
Referring Image Segmentation (RIS) is an advanced vision-language task that involves identifying and segmenting objects within an image as described by free-form text descriptions.…
Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation
Seungho Lee, Hwijeong Lee, Hyunjung Shim
We address the challenges of the semi-supervised LiDAR segmentation (SSLS) problem, particularly in low-budget scenarios. The two main issues in low-budget SSLS are the poor-qualit…
Understanding Multi-Granularity for Open-Vocabulary Part Segmentation
Jiho Choi, Seonho Lee, Seungho Lee +2
Open-vocabulary part segmentation (OVPS) is an emerging research area focused on segmenting fine-grained entities using diverse and previously unseen vocabularies. Our study highli…
Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels
Seungho Lee, Seoungyoon Kang, Hyunjung Shim
This study demonstrates a cost-effective approach to semantic segmentation using self-supervised vision transformers (SSVT). By freezing the SSVT backbone and training a lightweigh…
Weakly Supervised Semantic Segmentation for Driving Scenes
Dongseob Kim, Seungho Lee, Junsuk Choe +1
State-of-the-art techniques in weakly-supervised semantic segmentation (WSSS) using image-level labels exhibit severe performance degradation on driving scene datasets such as City…