Transformer trackers have achieved impressive advancements recently, where the attention
mechanism plays an important role. However, the independent correlation computation in …

This paper presents a novel cost aggregation network, called Volumetric Aggregation with
Transformers (VAT), for few-shot segmentation. The use of transformers can benefit …

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 …

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 …

Few-shot semantic segmentation is the task of learning to locate each pixel of the novel
class in the query image with only a few annotated support images. The current correlation …

Geometric verification is considered a de facto solution for the re-ranking task in image
retrieval. In this study, we propose a novel image retrieval re-ranking network named …

We tackle the problem of establishing dense pixel-wise correspondences between a pair of
images. In this work, we introduce Dual-Resolution Correspondence Networks (DualRC …

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 …

We propose a novel cost aggregation network, called Cost Aggregation Transformers
(CATs), to find dense correspondences between semantically similar images with additional …

Despite advances in feature representation, leveraging geometric relations is crucial for
establishing reliable visual correspondences under large variations of images. In this work …