4 citations · 7 across the 4 of their papers we have counts for
8 papers
TransforMatcher: Match-to-Match Attention for Semantic Correspondence
Seungwook Kim, Juhong Min, Minsu Cho
Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations. In this wor…
Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence
Juhong Min, Seungwook Kim, Minsu Cho
Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this wor…
Relational Embedding for Few-Shot Classification
Dahyun Kang, Heeseung Kwon, Juhong Min +1
We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective. Our method leverages relational p…
Hypercorrelation Squeeze for Few-Shot Segmentation
Juhong Min, Dahyun Kang, Minsu Cho
Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class. This challenging task r…
Convolutional Hough Matching Networks
Juhong Min, Minsu Cho
Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this wor…
Learning to Compose Hypercolumns for Visual Correspondence
Juhong Min, Jongmin Lee, Jean Ponce +1
Feature representation plays a crucial role in visual correspondence, and recent methods for image matching resort to deeply stacked convolutional layers. These models, however, ar…