4 citations · 8 across the 3 of their papers we have counts for
4 papers
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…
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Sunghwan Hong, Jisu Nam, Seokju Cho +4
Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters. Th…
Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence
Sunghwan Hong, Seokju Cho, Seungryong Kim +1
We present a novel architecture for dense correspondence. The current state-of-the-art are Transformer-based approaches that focus on either feature descriptors or cost volume aggr…
AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning
Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee +5
Semi-supervised learning (SSL) has recently proven to be an effective paradigm for leveraging a huge amount of unlabeled data while mitigating the reliance on large labeled data. C…