1 citations · 1 across the 5 of their papers we have counts for
6 papers
AgentRVOS: Reasoning over Object Tracks for Zero-Shot Referring Video Object Segmentation
Woojeong Jin, Jaeho Lee, Heeseong Shin +3
Referring Video Object Segmentation (RVOS) aims to segment a target object throughout a video given a natural language query. Training-free methods for this task follow a common pi…
Visual Representation Alignment for Multimodal Large Language Models
Heeji Yoon, Jaewoo Jung, Junwan Kim +10
Multimodal large language models (MLLMs) trained with visual instruction tuning have achieved strong performance across diverse tasks, yet they remain limited in vision-centric tas…
Exploring Conditions for Diffusion models in Robotic Control
Heeseong Shin, Byeongho Heo, Dongyoon Han +2
While pre-trained visual representations have significantly advanced imitation learning, they are often task-agnostic as they remain frozen during policy learning. In this work, we…
Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformers
Chaehyun Kim, Heeseong Shin, Eunbeen Hong +5
Text-to-image diffusion models excel at translating language prompts into photorealistic images by implicitly grounding textual concepts through their cross-modal attention mechani…
S^4M: Boosting Semi-Supervised Instance Segmentation with SAM
Heeji Yoon, Heeseong Shin, Eunbeen Hong +4
Semi-supervised instance segmentation poses challenges due to limited labeled data, causing difficulties in accurately localizing distinct object instances. Current teacher-student…
Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels
Heeseong Shin, Chaehyun Kim, Sunghwan Hong +4
Large-scale vision-language models like CLIP have demonstrated impressive open-vocabulary capabilities for image-level tasks, excelling in recognizing what objects are present. How…