- ALWOD: Active Learning for Weakly-Supervised Object Detection [2023, ICCV]
- Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation [2023, ICCV]
- Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking [2023, ICCV]
- Active Learning for Lane Detection: A Knowledge Distillation Approach [2021, ICCV]
- Contrastive Coding for Active Learning under Class Distribution Mismatch [2021, ICCV/TPAMI]
- Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling [2021, ICCV]
- Multi-Anchor Active Domain Adaptation for Semantic Segmentation [2021, ICCV]：
- Active Learning for Deep Object Detection via Probabilistic Modeling [2021, ICCV]
- ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation [2021, ICCV]
- S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation [2021, ICCV]
- Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings [2021, ICCV]
- Active Universal Domain Adaptation [2021, ICCV]: There are unknown class in the target domain.
- Semi-supervised Active Learning for Semi-supervised Models: Exploit Adversarial Examples with Graph-based Virtual Labels [2021, ICCV]
- Variational Adversarial Active Learning [ICCV, 2019]:
- Active Decision Boundary Annotation with Deep Generative Models [2017, ICCV]
- Active learning with Gaussian Processes for object categorization [2007, ICCV]