The rapid development of deep learning provides great convenience for production and life.
However, the massive labels required for training models limits further development. Few …

A two-stage training paradigm consisting of sequential pre-training and meta-training stages
has been widely used in current few-shot learning (FSL) research. Many of these methods …

We introduce the integrative task of few-shot classification and segmentation (FS-CS) that
aims to both classify and segment target objects in a query image when the target classes …

Abstract Vision Transformers (ViTs) emerge to achieve impressive performance on many
data-abundant computer vision tasks by capturing long-range dependencies among local …

Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories
with the help of limited available samples. Undoubtedly, this task inherits the main …

Few-shot learning is a challenging problem since only a few examples are provided to
recognize a new class. Several recent studies exploit additional semantic information, eg …

Abstract" A picture is worth a thousand words", significantly beyond mere a categorization.
Accompanied by that, many patches of the image could have completely irrelevant …

Single image-level annotations only correctly describe an often small subset of an image's
content, particularly when complex real-world scenes are depicted. While this might be …

We introduce LocoTrack, a highly accurate and efficient model designed for the task of
tracking any point (TAP) across video sequences. Previous approaches in this task often rely …

This paper presents new hierarchically cascaded transformers that can improve data
efficiency through attribute surrogates learning and spectral tokens pooling. Vision …