- Active Exploration of Multimodal Complementarity for Few-Shot Action Recognition [2023, CVPR]
- Hybrid Active Learning via Deep Clustering for Video Action Detection [2023, CVPR]
- Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm [2023, CVPR]
- MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation [2023, CVPR]
- Divide and Adapt: Active Domain Adaptation via Customized Learning [2023, CVPR]
- Bi3D: Bi-Domain Active Learning for Cross-Domain 3D Object Detection [2023, CVPR]
- Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint [2022, CVPR]
- One-bit Active Query with Contrastive Pairs [2022, CVPR]
- Continual Active Adaptation to Evolving Distributional Shifts [2022, CVPR]
- Active Learning by Feature Mixing [2022, CVPR]
- Meta Agent Teaming Active Learning for Pose Estimation [2022, CVPR]
- One-bit Active Query with Contrastive Pairs [2022, CVPR]
- Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach [2021, CVPR]
- Revisiting Superpixels for Active Learning in Semantic Segmentation with Realistic Annotation Costs [2021, CVPR]
- Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets [2021, CVPR]
- Multiple Instance Active Learning for Object Detection [2021, CVPR]
- Sequential Graph Convolutional Network for Active Learning [2021, CVPR]
- Task-Aware Variational Adversarial Active Learning [2021, CVPR]：
- VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning [2021, CVPR]:
- Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision [2020, CVPR]
- State-Relabeling Adversarial Active Learning [2020, CVPR]:
- ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation [2020, CVPR]
- Learning Loss for Active Learning [2019, CVPR]
- Learning loss for active learning [2019, CVPR]
- The power of ensembles for active learning in image classification [2018, CVPR]
- Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally [CVPR, 2017]:
- Active Image Segmentation Propagation [2016, CVPR]
- Adaptive active learning for image classification [CVPR, 2013]
- RALF: A Reinforced Active Learning Formulation for Object Class Recognition [2012, CVPR]
- Multi-Class Active Learning for Image Classification [CVPR, 2009]
- Two-dimensional active learning for image classification [2008, CVPR]: