Papers with Code 아카이브

Few-Shot Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 1
평가표: 10
보존된 평가 기록: 110

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.

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks

Label, Verify, Correct: A Simple Few Shot Object Detection Method

Grounded Language-Image Pre-training

A Survey of Deep Learning for Low-Shot Object Detection

AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration

Few-Shot Object Detection via Association and DIscrimination

Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object Detection

A Comparative Review of Recent Few-Shot Object Detection Algorithms

Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection

A Survey of Self-Supervised and Few-Shot Object Detection

Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference

Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images

Towards Generalized and Incremental Few-Shot Object Detection

Few-Shot Object Detection by Attending to Per-Sample-Prototype

DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection

Dynamic Relevance Learning for Few-Shot Object Detection

Towards Accurate Localization by Instance Search

DETReg: Unsupervised Pretraining with Region Priors for Object Detection

Generalized Few-Shot Object Detection without Forgetting

Class-Incremental Few-Shot Object Detection

Hallucination Improves Few-Shot Object Detection

Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment

Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection

Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation

FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding

Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection

Universal-Prototype Enhancing for Few-Shot Object Detection

Dual-Awareness Attention for Few-Shot Object Detection

Few-Shot Learning for Road Object Detection

MM-FSOD: Meta and metric integrated few-shot object detection

AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection

Cooperating RPN's Improve Few-Shot Object Detection

Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest

Restoring Negative Information in Few-Shot Object Detection

A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.