Learning Cascaded Detection Tasks with
Weakly-Supervised Domain Adaptation

Niklas Hanselmann 1,2 Nick Schneider 1 Benedikt Ortelt 3 Andreas Geiger 2,4
1 Mercdes-Benz AG R&D 2 University of Tübingen
3 Robert Bosch GmbH 4 Max Planck Institute for Intelligent Systems Tübingen
IV 2021



TL:DR: We propose a weakly-supervised domain-adaptation setting that enables learning cascaded detection tasks at a reduced annotation effort while still achieving competitive performance by utilizing weak 2D bounding box labels in both domains.
In order to handle the challenges of autonomous driving, deep learning has proven to be crucial in tackling increasingly complex tasks, such as 3D detection or instance segmentation. State-of-the-art approaches for image-based detection tasks tackle this complexity by operating in a cascaded fashion: they first extract a 2D bounding box based on which additional attributes, e.g. instance masks, are inferred. While these methods perform well, a key challenge remains the lack of accurate and cheap annotations for the growing variety of tasks. Synthetic data presents a promising solution but, despite the effort in domain adaptation research, the gap between synthetic and real data remains an open problem. In this work, we propose a weakly supervised domain adaptation setting which exploits the structure of cascaded detection tasks. In particular, we learn to infer the attributes solely from the source domain while leveraging 2D bounding boxes as weak labels in both domains to explain the domain shift. We further encourage domain-invariant features through class-wise feature alignment using ground-truth class information, which is not available in the unsupervised setting. As our experiments demonstrate, the approach is competitive with fully supervised settings while outperforming unsupervised adaptation approaches by a large margin.
A Weakly-Supervised Domain Adaptation Setting
Qualitative Results
Below we show a qualitative comparison of models trained in our setting to models trained on the source domain only without any adaptation and models trained using an unsupervised domain adaptation method for object detection (DAFRCNN, Chen et al.) on the cascaded detection tasks of monocular 3D detection and instance segmentation.
Monocular 3D Detection
Instance Segmentation

This publication was created as part of the research project "KI Delta Learning" (project number: 19A19013A) funded by the Federal Ministry for Economic Affairs and Energy (BMWi) on the basis of a decision by the German Bundestag.

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