We address the problem of semantic correspondence, that is, establishing a dense flow field
between images depicting different instances of the same object or scene category. We …

Deep features have been proven powerful in building accurate dense semantic
correspondences in various previous works. However, the multi-scale and pyramidal …

We address estimating dense correspondences between two images depicting different but
semantically related scenes. End-to-end trainable deep neural networks incorporating …

This paper presents a deep architecture for dense semantic correspondence, called
pyramidal affine regression networks (PARN), that estimates locally-varying affine …

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 …

Despite significant progress of deep learning in recent years, state-of-the-art semantic
matching methods still rely on legacy features such as SIFT or HoG. We argue that the …

Establishing visual correspondences under large intra-class variations, which is often
referred to as semantic correspondence or semantic matching, remains a challenging …

We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic
correspondence. To robustly match points among different instances within the same object …

Finding dense semantic correspondence is a fundamental problem in computer vision,
which remains challenging in complex scenes due to background clutter, extreme intra-class …

In this paper we address the challenge of matching semantically similar keypoints across
image pairs. Existing research indicates that the intermediate output of the UNet within the …