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Transfer_Learning_and_Domain_Adaptation.md

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Transfer Learning and Domain Adaptation

  • [SVCW] Multi-view People Detection in Large Scenes via Supervised View-wise Contribution Weighting (AAAI24) [paper][code]
  • [MDKNet] Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting (T-NNLS) [paper][code]GitHub stars
  • [DCANet] Towards Learning Multi-domain Crowd Counting (T-CSVT) [paper][code]GitHub stars
  • AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting (TMM) [paper]
  • [CVCS] Cross-View Cross-Scene Multi-View Crowd Counting (CVPR) [paper]
  • Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting (ACM MM)
  • [ASNet] Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network (ACM MM) [paper]
  • [DKPNet] Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (ICCV) [paper][code]GitHub stars
  • [SDNet] Towards A Universal Model for Cross-Dataset Crowd Counting (ICCV) [paper]
  • Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation [paper]
  • Leveraging Self-Supervision for Cross-Domain Crowd Counting [paper]
  • [EDIREC-Net] Error-Aware Density Isomorphism Reconstruction for Unsupervised Cross-Domain Crowd Counting (AAAI) [paper][code]GitHub stars
  • Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting (TNNLS) [paper]
  • [FSC] Focus on Semantic Consistency for Cross-domain Crowd Understanding (ICASSP) [paper]
  • [FSSA] Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning (WACV) [paper]
  • [DACC] Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction (TNNLS) [paper]
  • Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance (TCYB) [paper]
  • [OSSS] One-Shot Scene-Specific Crowd Counting (BMVC) [paper]
  • [CODA] CODA: Counting Objects via Scale-aware Adversarial Density Adaption (ICME) [paper][code]
  • [CCWld, SFCN] Learning from Synthetic Data for Crowd Counting in the Wild (CVPR2019) [paper] [Project] [arxiv]
  • Crowd Counting with Density Adaption Networks [paper]