Few-Shot Object Detection
77 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
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.
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.
LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection
lexisnexis-risk-open-source/ledetection
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Few-shot object detection (FSOD) is a challenging problem aimed at detecting novel concepts from few exemplars.
Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach
In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes.
Few-Shot Object Detection via Variational Feature Aggregation
csuhan/vfa
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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 sensitive to the variance of novel examples.
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework.
Proposal Distribution Calibration for Few-Shot Object Detection
bohao-lee/pdc
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Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging.
Multi-Modal Few-Shot Temporal Action Detection
sauradip/muppet
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In this work, we introduce a new multi-modality few-shot (MMFS) TAD problem, which can be considered as a marriage of FS-TAD and ZS-TAD by leveraging few-shot support videos and new class names jointly.
Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection
zs123-lang/tenet
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To address these drawbacks, we propose a Time-rEversed diffusioN tEnsor Transformer (TENET), which i) forms high-order tensor representations that capture multi-way feature occurrences that are highly discriminative, and ii) uses a transformer that dynamically extracts correlations between the query image and the entire support set, instead of a single average-pooled support embedding.
