1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Bridging Audio and Vision: Zero-Shot Audiovisual Segmentation by Connecting Pretrained Models
Seung-jae Lee, Paul Hongsuck Seo
Audiovisual segmentation (AVS) aims to identify visual regions corresponding to sound sources, playing a vital role in video understanding, surveillance, and human-computer interac…
Multi-Granularity Video Object Segmentation
Sangbeom Lim, Seongchan Kim, Seungjun An +3
Current benchmarks for video segmentation are limited to annotating only salient objects (i.e., foreground instances). Despite their impressive architectural designs, previous work…
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…
Pseudo-RIS: Distinctive Pseudo-supervision Generation for Referring Image Segmentation
Seonghoon Yu, Paul Hongsuck Seo, Jeany Son
We propose a new framework that automatically generates high-quality segmentation masks with their referring expressions as pseudo supervisions for referring image segmentation (RI…