https://github.com/wonbeomjang/yolov5-knowledge-distillation
Distilling Object Detectors with Fine-grained Feature Imitation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Replace the cat with a British Shorthair cat of the breed with bulging yellow eyes
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Distilling Object Detectors with Fine-grained Feature Imitation
First image then video: A two-stage network for spatiotemporal video denoising
LSMVOS: Long-Short-Term Similarity Matching for Video Object
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Learning Hierarchical Semantic Image Manipulation through Structured Representations
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Learning regression and verification networks for long-term visual tracking
Bounding Box Regression with Uncertainty for Accurate Object Detection
Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
R-FCN: Object Detection via Region-based Fully Convolutional Networks
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
Learning to Manipulate Deformable Objects without Demonstrations
Object Tracking by Jointly Exploiting Frame and Event Domain
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.