- meta-learning·Last edited by Michael Koo on Dec 19, 2021Linked with GitHubContributed by
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MetaDet
introduces a weight prediction model to predict the model parameters from its few-shot parameters. the meta-learning strategy is designed to disentangle the learning of class-agnostic and class-specific weight parameters. this work focuses on weight parameter generation/prediction for the detector network (box head) by learning a meta-model that regresses the class-specific parameters trained from few-shot to class-specific parameters trained from large base classes. Training in the first stage, a detector is trained on large base classes, specifically to learn the class-agnostic parameters. in the second stage, only the class-specific parameters are learned while the class-agnostic parameters are fixed. The meta model T is trained by receiving the class-specific weights in the last layer and class-specific weights from few-shot training as inputs, where T is parameterized by the class-agnostic weights. Loss function for training T is:
Dec 20, 2021A Comparative Review of Few-Shot Object Detection
this is by far the only survey that systematically compares few-shot object detection methods. Taxonomy (based on dataset settings) In terms of novel classes, the problems can be defined as: LS-FSOD: a small novel set data and an optional dataset without target supervision to learn generic notions. SS-FSOD: has an extra target-domain data without annotations (an additional unlabelled examples) WS-FSOD: a small novel set of data with image-level labels (weakly labelled, sometimes some unlaballed novel set and a base set may be included to compensate for the inaccurate supervisory signals) Usually, base is usually for learning task-agnostic notions, where as learning from whether weakly labelled novel set or unlabelled novel set is for learning task-specific guidance
Dec 4, 2021Weakly Supervised Object Localisation and Detection: A Survey
Dec 3, 2021Ideas on improving FSOD
Dec 1, 2021
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