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 …

Deep learning has had remarkable success in robotic perception, but its data-centric nature
suffers when it comes to generalizing to ever-changing environments. By contrast, physics …

Few-Shot Instance Segmentation (FSIS) requires detecting and segmenting novel classes
with limited support examples. Existing methods based on Region Proposal Networks …

The increasing prevalence of unmanned aerial vehicles (UAVs), commonly known as
drones, has generated a demand for reliable detection systems. The inappropriate use of …

Recently, few-shot action recognition has progressed significantly, as it has learned the
feature discriminability and designed suitable comparison methods. Still, there are the …

This article presents a dataset collected from the subterranean (SubT) environment with a
current state-of-the-art sensors required for autonomous navigation. The dataset includes …

Adapting object detectors learned with sufficient supervision to novel classes under low data
regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step …

Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object
instances of novel categories within a target domain. Recent advances in FSOD focus on …

Object detection is a critical field in computer vision focusing on accurately identifying and
locating specific objects in images or videos. Traditional methods for object detection rely on …