Few Shot Object Detection


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.

Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detection

Add code
Jun 06, 2025
Viaarxiv icon

Interpreting Biomedical VLMs on High-Imbalance Out-of-Distributions: An Insight into BiomedCLIP on Radiology

Add code
Jun 17, 2025
Viaarxiv icon

Dataset of News Articles with Provenance Metadata for Media Relevance Assessment

Add code
Jun 11, 2025
Viaarxiv icon

Locality-Aware Zero-Shot Human-Object Interaction Detection

Add code
May 26, 2025
Viaarxiv icon

Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation

Add code
May 20, 2025
Viaarxiv icon

Hallucinate, Ground, Repeat: A Framework for Generalized Visual Relationship Detection

Add code
Jun 06, 2025
Viaarxiv icon

Roboflow100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language Models

Add code
May 27, 2025
Viaarxiv icon

Training-free zero-shot 3D symmetry detection with visual features back-projected to geometry

Add code
May 30, 2025
Viaarxiv icon

Joint User Association and Beamforming Design for ISAC Networks with Large Language Models

Add code
Jun 05, 2025
Viaarxiv icon

Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset

Add code
May 26, 2025
Viaarxiv icon