Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation
We describe an information-driven active selection approach to determine which detectors to deploy at which location in which frame of a video to minimize semantic class label uncertainty at every pixel, with the smallest computational cost that ensures a given uncertainty bound. We show minimal performance reduction compared to a "paragon" algorithm running all detectors at all locations in all frames, at a small fraction of the computational cost. Our method can handle uncertainty in the labeling mechanism, so it can handle both "oracles" (manual annotation) or noisy detectors (automated annotation).
Code (0)
등록된 구현이 없습니다.
Tasks
AllSimilar Papers 제목 키워드 기반
A model-agnostic active learning approach for animal detection from camera traps
Smart data selection is becoming increasingly important in data-driven machine learning. Active learning offers a promising solution by allowing machine learning models to be effectively trained with optimal data includi…
Active LearningInteractive Prototype Learning for Egocentric Action Recognition
Egocentric video recognition is a challenging task that requires to identify both the actor's motion and the active object that the actor interacts with. Recognizing the active object is particularly hard due to the …
Action RecognitionObjectVideo RecognitionHolding-Based Evaluation upon Actively Managed Stock Mutual Funds in China
We analyze actively managed mutual funds in China from 2005 to 2017. We develop performance measures for asset allocation and selection. We find that stock selection ability from holding-based model is positively correla…
regressionLocation-Aware Box Reasoning for Anchor-Based Single-Shot Object Detection
In the majority of object detection frameworks, the confidence of instance classification is used as the quality criterion of predicted bounding boxes, like the confidence-based ranking in non-maximum suppression (NMS). …
General ClassificationObjectobject-detectionObject Detection+1Simultaneous Contact Selection and Planning for Contact-Rich Manipulation with Cascaded Optimization
We propose an optimization-based framework for robust contact-rich manipulation. Recent contact-implicit methods enable online hybrid planning across contact modes, allowing closed-loop manipulation for a given target st…