Few-Shot Object Detection
75 papers with code • 8 benchmarks • 7 datasets
Few-Shot Object Detection is a computer vision task that involves detecting objects in images with limited training data. The goal is to train a model on a few examples of each object class and then use the model to detect objects in new images.
Benchmarks
These leaderboards are used to track progress in Few-Shot Object Detection
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Libraries
Use these libraries to find Few-Shot Object Detection models and implementations
MS COCO
LVIS
FSOD
ELEVATER
VizWiz-FewShot
Latest papers
AirShot: Efficient Few-Shot Detection for Autonomous Exploration
Few-shot object detection has drawn increasing attention in the field of robotic exploration, where robots are required to find unseen objects with a few online provided examples.
Cross-domain Multi-modal Few-shot Object Detection via Rich Text
zshanggu/cdmm
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Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks due to generating richer features.
Fine-Grained Prototypes Distillation for Few-Shot Object Detection
wangchen1801/fpd
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However, the class-level prototypes are difficult to precisely generate, and they also lack detailed information, leading to instability in performance. New methods are required to capture the distinctive local context for more robust novel object detection.
TIDE: Test Time Few Shot Object Detection
deku-0621/tide
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Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object instances of novel categories within a target domain.
Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
INFINIQ-AI1/RISF
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The former adapts CLIP, which performs zero-shot classification, to re-score the classification scores of a detector using image-class similarities, the latter is modified classification loss considering the punishment for fake backgrounds as well as confusing categories on a generalized few-shot object detection dataset.
Detect Everything with Few Examples
mlzxy/devit
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DE-ViT establishes new state-of-the-art results on all benchmarks.
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects
zhu-xlab/st-fsod
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In this context, few-shot object detection (FSOD) has emerged as a promising direction, which aims at enabling the model to detect novel objects with only few of them annotated.
Improved Region Proposal Network for Enhanced Few-Shot Object Detection
zshanggu/htrpn
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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.
Multi-modal Queried Object Detection in the Wild
yifanxu74/mq-det
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NeurIPS 2023
To address the learning inertia problem brought by the frozen detector, a vision conditioned masked language prediction strategy is proposed.
Identification of Novel Classes for Improving Few-Shot Object Detection
zshanggu/htrpn
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Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects.
