Incremental Multi-Target Domain Adaptation for Object Detection with
Efficient Domain Transfer
- URL: http://arxiv.org/abs/2104.06476v1
- Date: Tue, 13 Apr 2021 19:35:54 GMT
- Title: Incremental Multi-Target Domain Adaptation for Object Detection with
Efficient Domain Transfer
- Authors: Le Thanh Nguyen-Meidine, Madhu Kiran, Marco Pedersoli, Jose Dolz,
Louis-Antoine Blais-Morin, Eric Granger
- Abstract summary: Techniques for multi-target domain adaptation (MTDA) seek to adapt a recognition model such that it can generalize well across multiple target domains.
Key challenges include the lack of bounding box annotations for target data, knowledge corruption, and the growing resource requirements needed to train accurate deep detection models.
We propose a new Incremental MTDA technique for object detection that can adapt a detector to multiple target domains, one at a time, without having to retain data of previously-learned target domains.
- Score: 15.000304613769108
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Techniques for multi-target domain adaptation (MTDA) seek to adapt a
recognition model such that it can generalize well across multiple target
domains. While several successful techniques have been proposed for
unsupervised single-target domain adaptation (STDA) in object detection,
adapting a model to multiple target domains using unlabeled image data remains
a challenging and largely unexplored problem. Key challenges include the lack
of bounding box annotations for target data, knowledge corruption, and the
growing resource requirements needed to train accurate deep detection models.
The later requirements are augmented by the need to retraining a model with
previous-learned target data when adapting to each new target domain.
Currently, the only MTDA technique in literature for object detection relies on
distillation with a duplicated model to avoid knowledge corruption but does not
leverage the source-target feature alignment after UDA. To address these
challenges, we propose a new Incremental MTDA technique for object detection
that can adapt a detector to multiple target domains, one at a time, without
having to retain data of previously-learned target domains. Instead of
distillation, our technique efficiently transfers source images to a joint
target domains' space, on the fly, thereby preserving knowledge during
incremental MTDA. Using adversarial training, our Domain Transfer Module (DTM)
is optimized to trick the domain classifiers into classifying source images as
though transferred into the target domain, thus allowing the DTM to generate
samples close to a joint distribution of target domains. Our proposed technique
is validated on different MTDA detection benchmarks, and results show it
improving accuracy across multiple domains, despite the considerable reduction
in complexity.
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