https://github.com/hula-ai/DAMA
Exploring Plain Vision Transformer Backbones for Object Detection
Papers with Code 아카이브
Few-Shot Object Detection
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/idea-research/grounded-sam-2
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
https://github.com/jshtok/RepMet
RepMet: Representative-based metric learning for classification and one-shot object detection
https://github.com/kdexd/coco-rem
Exploring Plain Vision Transformer Backbones for Object Detection
https://github.com/leokarlin/StarNet
StarNet: towards Weakly Supervised Few-Shot Object Detection
https://github.com/megvii-research/fsce
https://github.com/michaelisc/cluttered-omniglot
https://github.com/microsoft/esvit
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/microsoft/klite
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/microsoft/unicl
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/miquel-espinosa/samantics
No time to train! Training-Free Reference-Based Instance Segmentation
https://github.com/mit-han-lab/efficientvit
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
https://github.com/pierlj/proto_faster_rcnn
Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images
https://github.com/pwc-1/Paper-9/tree/main/1/vitdet
Exploring Plain Vision Transformer Backbones for Object Detection
https://github.com/pwc-1/Paper-9/tree/main/6/CME
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
https://github.com/sincerass/mvlpt
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/vitae-transformer/qformer
Exploring Plain Vision Transformer Backbones for Object Detection
https://github.com/yangyucheng000/CME
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
https://github.com/yanxp/MetaR-CNN
https://github.com/yuhongtian17/spatial-transform-decoupling
Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
https://gitlab.com/birder/birder
Exploring Plain Vision Transformer Backbones for Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.