Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection
This paper studies how to adapt a computer vision object detector to an unknown environment under both a robot navigation time and annotation budget constraint. Our approach selects informative robot trajectories and image samples to retrain the detector, explicitly targeting its failure cases. Formally, the approach is an embodied variant of batch active learning, where at each round an agent has a limited navigation budget to collect candidate samples and a limited annotation budget for the most relevant images. We leverage spatial consistency to identify images with inconsistent labels, whi
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- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Embodied Active Learning under Limited Annotation and Naviga” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Hadrien Crassous →
“Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection”
- LinkedLinked via arxiv author · 85%Mohamed Yassine Kabouri →
“Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection”
- LinkedLinked via arxiv author · 85%Minahil Raza →
“Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection”
- LinkedLinked via arxiv author · 85%Joni Pajarinen →
“Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection”
- LinkedLinked via arxiv author · 85%Riad Akrour →
“Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection”