1 citations · 1 across the 2 of their papers we have counts for
4 papers
Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation for Semantic Segmentation
Inseop Chung, Jayeon Yoo, Nojun Kwak
"Self-training" has become a dominant method for semantic segmentation via unsupervised domain adaptation (UDA). It creates a set of pseudo labels for the target domain to give exp…
Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation
Inseop Chung, Daesik Kim, Nojun Kwak
We propose a novel method that tackles the problem of unsupervised domain adaptation for semantic segmentation by maximizing the cosine similarity between the source and the target…
Feature-map-level Online Adversarial Knowledge Distillation
Inseop Chung, SeongUk Park, Jangho Kim +1
Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities. T…
Feature Fusion for Online Mutual Knowledge Distillation
Jangho Kim, Minsung Hyun, Inseop Chung +1
We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated…