79 citations · 111 across the 14 of their papers we have counts for
8 papers · 1 filter
What, How, and When Should Object Detectors Update in Continually Changing Test Domains?
Jayeon Yoo, Dongkwan Lee, Inseop Chung +2
It is a well-known fact that the performance of deep learning models deteriorates when they encounter a distribution shift at test time. Test-time adaptation (TTA) algorithms have…
Open Domain Generalization with a Single Network by Regularization Exploiting Pre-trained Features
Inseop Chung, KiYoon Yoo, Nojun Kwak
Open Domain Generalization (ODG) is a challenging task as it not only deals with distribution shifts but also category shifts between the source and target datasets. To handle this…
Unsupervised Domain Adaptation for One-stage Object Detector using Offsets to Bounding Box
Jayeon Yoo, Inseop Chung, Nojun Kwak
Most existing domain adaptive object detection methods exploit adversarial feature alignment to adapt the model to a new domain. Recent advances in adversarial feature alignment st…
End-to-End Multi-Object Detection with a Regularized Mixture Model
Jaeyoung Yoo, Hojun Lee, Seunghyeon Seo +2
Recent end-to-end multi-object detectors simplify the inference pipeline by removing hand-crafted processes such as non-maximum suppression (NMS). However, during training, they st…
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…
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image
Jaeyoung Yoo, Hojun Lee, Inseop Chung +2
In multi-object detection using neural networks, the fundamental problem is, "How should the network learn a variable number of bounding boxes in different input images?". Previous…
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…