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Few-shot object detection: A survey
Deep learning approaches have recently raised the bar in many fields, from Natural
Language Processing to Computer Vision, by leveraging large amounts of data. However …
Language Processing to Computer Vision, by leveraging large amounts of data. However …
Few-shot object detection: Research advances and challenges
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 …
which aims to accurately identify and locate a specific object from images or videos. Such …
Sigma: Semantic-complete graph matching for domain adaptive object detection
Abstract Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn an
object detector generalizing to a novel domain free of annotations. Recent advances align …
object detector generalizing to a novel domain free of annotations. Recent advances align …
Few-shot object detection with fully cross-transformer
Few-shot object detection (FSOD), with the aim to detect novel objects using very few
training examples, has recently attracted great research interest in the community. Metric …
training examples, has recently attracted great research interest in the community. Metric …
Meta-detr: Image-level few-shot detection with inter-class correlation exploitation
Few-shot object detection has been extensively investigated by incorporating meta-learning
into region-based detection frameworks. Despite its success, the said paradigm is still …
into region-based detection frameworks. Despite its success, the said paradigm is still …
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 …
Few-shot object detection via variational feature aggregation
As few-shot object detectors are often trained with abundant base samples and fine-tuned
on few-shot novel examples, the learned models are usually biased to base classes and …
on few-shot novel examples, the learned models are usually biased to base classes and …
Few-shot object detection with foundation models
Few-shot object detection (FSOD) aims to detect objects with only a few training examples.
Visual feature extraction and query-support similarity learning are the two critical …
Visual feature extraction and query-support similarity learning are the two critical …
Few-shot object detection via association and discrimination
Object detection has achieved substantial progress in the last decade. However, detecting
novel classes with only few samples remains challenging, since deep learning under low …
novel classes with only few samples remains challenging, since deep learning under low …
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 …