Estimating dense visual correspondences between objects with intra-class variation,
deformations and background clutter remains a challenging problem. Thanks to the …

We explore semantic correspondence estimation through the lens of unsupervised learning.
We thoroughly evaluate several recently proposed unsupervised methods across multiple …

Establishing semantic correspondence is a core problem in computer vision and remains
challenging due to large intra-class variations and lack of annotated data. In this paper, we …

Recent advances show that Generative Adversarial Networks (GANs) can synthesize
images with smooth variations along semantically meaningful latent directions, such as …

Conventional techniques to establish dense correspondences across visually or
semantically similar images focused on designing a task-specific matching prior, which is …

The key challenge in learning dense correspondences lies in the lack of ground-truth
matches for real image pairs. While photometric consistency losses provide unsupervised …

Recent self-supervised models produce visual features that are not only effective at
encoding image-level but also pixel-level semantics. They have been reported to obtain …

Transformer-based pre-trained models like BERT have achieved great progress on
Semantic Sentence Matching. Meanwhile, dependency prior knowledge has also shown …

We address the problem of establishing accurate correspondences between two images.
We present a flexible framework that can easily adapt to both geometric and semantic …

Image matching remains an important and challenging problem in computer vision,
especially for the dense correspondence estimation between images with high category …