Expanding object detector's Horizon: Incremental learning framework for object detection in videos

By Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, Leonid Sigal. 2015

49 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning

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Citations in the corpus

Extreme Clicking for Efficient Object Annotation (2017, background): “Training object class detectors from videos could bypass the need for manual bounding boxes, as the motion of the objects facilitates their automatic localization [49, 40, 41].”
Object-Proposal Evaluation Protocol is ‘Gameable’ (2015, background): “Types of Object Proposals: Object proposals can be broadly categorized into two categories: • Window scoring : In these methods, the space of all possible windows in an image is sampled to get a sub-set of the windows ( e.g ., via sliding window).”
Improving the Robustness of Deep Neural Networks via Stability Training (2016, background): “Finding such hard positives in video data for data augmentation has been used in [5, 4, 8] and has been found to improve predictive performance and consistency.”
Automatic Adaptation of Object Detectors to New Domains Using Self-Training (2019, background): “and selecting target samples based on the baseline detector confidence score [19, 54, 47, 10, 30, 6].”
Y Qualification Main task Y Return N N Y User qualified ? N Feedback Pass ? Pass ? Annotator training Annotating images (2017, background): “Training object class detectors from videos could bypass the need for manual bounding boxes, as the motion of the objects facilitates their automatic localization [49, 40, 41].”
Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision (2018, unknown): “These take the input proposals and classify (or discard) them with some labels [46, 114, 104, 45, 101, 21, 58, 36, 37].”
Does Data Augmentation Lead to Positive Margin? (2019, background): “Others have augmented datasets by adding labels to sparsely annotated videos (Misra et al., 2015; Kuznetsova et al., 2015; Prest et al., 2012).”