Jul 25, 2018 · We propose a novel loss function that encourages the network to output a single blob per object instance using point-level annotations only.

ECCV 2018 - Where are the Blobs: Counting by Localization with Point Supervision. This is a ServiceNow Research project that was started at Element AI.

Our method explicitly learns to localize object instances using only point-level annotations. The trained model then outputs blobs where each unique color ...

Our contributions are three-fold: (1) we propose a novel loss function that encourages the network to output a single blob per object instance using point-level ...

The loss function identifies the boundary splits (shown as yellow lines). Yellow blobs represent those with more than one object instance, and red blobs ...

This work proposes a detection-based method that does not need to estimate the size and shape of the objects and that outperforms regression-based methods ...

Nov 21, 2024 · The loss function identifies the boundary splits (shown as yellow lines). Yellow blobs represent those with more than one object instance, and ...

Jul 25, 2018 · Our method even outperforms those that use stronger supervision such as depth features, multi-point annotations, and bounding-box labels.

Where are the Blobs: Counting by Localization with Point Supervision ... However, we propose a detection-based method that does not need to estimate the size and ...

0.19, 0.99, 0.38, 2.20. Where are the Blobs: Counting by Localization with Point Supervision. 2018. 5. Fast-RCNN. 0.20, 1.13, 0.49, 2.78. Counting Everyday ...