A Few-Shot Sequential Approach for Object Counting
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In this work, we address the problem of few-shot multi-classobject counting with point-level annotations. The proposed techniqueleverages a class agnostic attention mechanism that sequentially attendsto objects in the image and extracts their relevant features. This pro-cess is employed on an adapted prototypical-based few-shot approachthat uses the extracted features to classify each one either as one of theclasses present in the support set images or as background. The proposedtechnique is trained on point-level annotations and uses a novel loss func-tion that disentangles class-dependent and class-agnostic aspects of themodel to help with the task of few-shot object counting. We presentour results on a variety of object-counting/detection datasets, includingFSOD and MS COCO. In addition, we introduce a new dataset thatis specifically designed for weakly supervised multi-class object count-ing/detection and contains considerably different classes and distribu-tion of number of classes/instances per image compared to the existingdatasets. We demonstrate the robustness of our approach by testing oursystem on a totally different distribution of classes from what it has beentrained on
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