Few-shot object detection: A comprehensive survey
Humans are able to learn to recognize new objects even from a few examples. In contrast,
training deep-learning-based object detectors requires huge amounts of annotated data. To …
training deep-learning-based object detectors requires huge amounts of annotated data. To …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Meta faster r-cnn: Towards accurate few-shot object detection with attentive feature alignment
Few-shot object detection (FSOD) aims to detect objects using only a few examples. How to
adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object …
adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object …
Supervised masked knowledge distillation for few-shot transformers
Abstract Vision Transformers (ViTs) emerge to achieve impressive performance on many
data-abundant computer vision tasks by capturing long-range dependencies among local …
data-abundant computer vision tasks by capturing long-range dependencies among local …
Digeo: Discriminative geometry-aware learning for generalized few-shot object detection
Generalized few-shot object detection aims to achieve precise detection on both base
classes with abundant annotations and novel classes with limited training data. Existing …
classes with abundant annotations and novel classes with limited training data. Existing …
Multi-modal queried object detection in the wild
We introduce MQ-Det, an efficient architecture and pre-training strategy design to utilize both
textual description with open-set generalization and visual exemplars with rich description …
textual description with open-set generalization and visual exemplars with rich description …
Explore the power of synthetic data on few-shot object detection
Few-shot object detection (FSOD) aims to expand an object detector for novel categories
given only a few instances for training. The few training samples restrict the performance of …
given only a few instances for training. The few training samples restrict the performance of …
Swin transformer based vehicle detection in undisciplined traffic environment
Intelligent vehicle detection (IVD) plays a prominent role in evolving an intelligent traffic
management system (ITMS). It can help to decrease the average waiting time at the traffic …
management system (ITMS). It can help to decrease the average waiting time at the traffic …
Fs-detr: Few-shot detection transformer with prompting and without re-training
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 …
(examples) depicting a novel class (not seen during training), the goal is to detect all of its …
Breaking immutable: Information-coupled prototype elaboration for few-shot object detection
Few-shot object detection, expecting detectors to detect novel classes with a few instances,
has made conspicuous progress. However, the prototypes extracted by existing meta …
has made conspicuous progress. However, the prototypes extracted by existing meta …