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Found 7 papers, 3 papers with code
Cross-domain Multi-modal Few-shot Object Detection via Rich Text
Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks due to generating richer features.
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples.
Decoupled DETR For Few-shot Object Detection
We also explore various types of skip connection between the encoder and decoder for DETR.
Improved Region Proposal Network for Enhanced Few-Shot Object Detection
Specifically, we develop a hierarchical ternary classification region proposal network (HTRPN) to localize the potential unlabeled novel objects and assign them new objectness labels to distinguish these objects from the base training dataset classes.
Identification of Novel Classes for Improving Few-Shot Object Detection
Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects.
Few-shot Object Detection with Refined Contrastive Learning
A pre-determination component is introduced to find out the Resemblance Group from novel classes which contains confusable classes.
Distinctive Self-Similar Object Detection
However, objects such as fire and smoke, pose challenges to object detection because of their non-solid and various shapes, and consequently difficult to truly meet requirements in practical fire prevention and control.
