Object detection as a subfield within computer vision has achieved remarkable progress,
which aims to accurately identify and locate a specific object from images or videos. Such …

Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared
with inductive few-shot learning, transductive models typically perform better as they …

Many skeletal action recognition models use GCNs to represent the human body by 3D
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …

We address the task of weakly-supervised few-shot image classification and segmentation,
by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed …

Object detection has achieved a huge breakthrough with deep neural networks and massive
annotated data. However, current detection methods cannot be directly transferred to the …

This paper is on Few-Shot Object Detection (FSOD), where given a few templates
(examples) depicting a novel class (not seen during training), the goal is to detect all of its …

We propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint
Temporal and Camera Viewpoint Alignment. To factor out misalignment between query and …

Visual representation based on covariance matrix has demonstrates its efficacy for image
classification by characterising the pairwise correlation of different channels in convolutional …

In instance image retrieval, considering local spatial information within an image has proven
effective to boost retrieval performance, as demonstrated by local visual descriptor based …

Traditional approaches for learning on categorical data underexploit the dependencies
between columns (aka fields) in a dataset because they rely on the embedding of data …