What question did this study set out to answer?

The survey aims to explore recent advancements in few-shot learning techniques applicable to video and 3D object detection.

Few-Shot Learning in Video and 3D Object Detection: A Survey

View Full Paper

Ask AI

Bookmark

Authors

This survey examines few-shot learning techniques for object detection, highlighting implications for data efficiency in various fields.

The survey aims to explore recent advancements in few-shot learning techniques applicable to video and 3D object detection.
Systematic survey of few-shot, semi-supervised, sparsely-supervised, and weakly-supervised approaches.
Analysis of techniques like tube proposals, temporal matching networks, and motion-guided methods for video detection.
Investigation of uncertainty-aware methods, geometric learning, and multimodal fusion for 3D detection.
Focus on foundation models and vision-language model integration across applications.
Achieved substantial gains in video detection, with average precision improving from 33 to 48 in few-shot scenarios.
Sparsely-supervised techniques performed competitively with only 2% of annotations for 3D object detection.
Highlighted data-efficient learning's potential to minimize annotation needs for real-world applications.

Ferdaus et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

This survey examines few-shot learning techniques for object detection, highlighting implications for data efficiency in various fields.

The survey aims to explore recent advancements in few-shot learning techniques applicable to video and 3D object detection.
Systematic survey of few-shot, semi-supervised, sparsely-supervised, and weakly-supervised approaches.
Analysis of techniques like tube proposals, temporal matching networks, and motion-guided methods for video detection.
Investigation of uncertainty-aware methods, geometric learning, and multimodal fusion for 3D detection.
Focus on foundation models and vision-language model integration across applications.
Achieved substantial gains in video detection, with average precision improving from 33 to 48 in few-shot scenarios.
Sparsely-supervised techniques performed competitively with only 2% of annotations for 3D object detection.
Highlighted data-efficient learning's potential to minimize annotation needs for real-world applications.

Ferdaus et al. (2026) studied this question.

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark