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Author  |
Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat | ||||
| Title | Weakly Supervised Multi-Object Tracking and Segmentation | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 125-133 | ||
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| Abstract | We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
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| Address | Virtual; January 2021 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | WACVW | ||
| Notes | ADAS; 600.118; 600.124 | Approved | no | ||
| Call Number | Admin @ si @ RPR2021 | Serial | 3548 | ||
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