activity
20202026
most citedExploiting Shape Cues for Weakly Supervised Semantic Segmentation

25 citations · 25 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDAR

Hoonhee Cho, Jae-Young Kang, Yuhwan Jeong +4

In this paper, we present DSERT-RoLL, a driving dataset that incorporates stereo event, RGB, and thermal cameras together with 4D radar and dual LiDAR, collected across diverse wea…

cs.CV2026

Label-Free Cross-Task LoRA Merging with Null-Space Compression

Wonyoung Lee, Wooseong Jeong, Kuk-Jin Yoon

Model merging combines independently fine-tuned checkpoints without joint multi-task training. In the era of foundation-model, fine-tuning with Low-Rank Adaptation (LoRA) is preval…

cs.CV2026

Preference-Aligned LoRA Merging: Preserving Subspace Coverage and Addressing Directional Anisotropy

Wooseong Jeong, Wonyoung Lee, Kuk-Jin Yoon

Merging multiple Low-Rank Adaptation (LoRA) modules is promising for constructing general-purpose systems, yet challenging because LoRA update directions span different subspaces a…

cs.CV2022★ 25 cited

Exploiting Shape Cues for Weakly Supervised Semantic Segmentation

Sungpil Kho, Pilhyeon Lee, Wonyoung Lee +2

Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise class predictions with only image-level labels for training. To this end, previous methods adopt the commo…

cs.CV2020

In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization

Minsong Ki, Youngjung Uh, Wonyoung Lee +1

Weakly supervised object localization (WSOL) aims to localize the target object using only the image-level supervision. Recent methods encourage the model to activate feature maps…