2 research outputs found
Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation
We present a novel approach to perform the unsupervised domain adaptation for
object detection through forward-backward cyclic (FBC) training. Recent
adversarial training based domain adaptation methods have shown their
effectiveness on minimizing domain discrepancy via marginal feature
distributions alignment. However, aligning the marginal feature distributions
does not guarantee the alignment of class conditional distributions. This
limitation is more evident when adapting object detectors as the domain
discrepancy is larger compared to the image classification task, e.g. various
number of objects exist in one image and the majority of content in an image is
the background. This motivates us to learn domain invariance for category level
semantics via gradient alignment. Intuitively, if the gradients of two domains
point in similar directions, then the learning of one domain can improve that
of another domain. To achieve gradient alignment, we propose Forward-Backward
Cyclic Adaptation, which iteratively computes adaptation from source to target
via backward hopping and from target to source via forward passing. In
addition, we align low-level features for adapting holistic color/texture via
adversarial training. However, the detector performs well on both domains is
not ideal for target domain. As such, in each cycle, domain diversity is
enforced by maximum entropy regularization on the source domain to penalize
confident source-specific learning and minimum entropy regularization on target
domain to intrigue target-specific learning. Theoretical analysis of the
training process is provided, and extensive experiments on challenging
cross-domain object detection datasets have shown the superiority of our
approach over the state-of-the-art
Unsupervised Domain Adaptation of Object Detectors: A Survey
Recent advances in deep learning have led to the development of accurate and
efficient models for various computer vision applications such as
classification, segmentation, and detection. However, learning highly accurate
models relies on the availability of large-scale annotated datasets. Due to
this, model performance drops drastically when evaluated on label-scarce
datasets having visually distinct images, termed as domain adaptation problem.
There is a plethora of works to adapt classification and segmentation models to
label-scarce target datasets through unsupervised domain adaptation.
Considering that detection is a fundamental task in computer vision, many
recent works have focused on developing novel domain adaptive detection
techniques. Here, we describe in detail the domain adaptation problem for
detection and present an extensive survey of the various methods. Furthermore,
we highlight strategies proposed and the associated shortcomings. Subsequently,
we identify multiple aspects of the problem that are most promising for future
research. We believe that this survey shall be valuable to the pattern
recognition experts working in the fields of computer vision, biometrics,
medical imaging, and autonomous navigation by introducing them to the problem,
and familiarizing them with the current status of the progress while providing
promising directions for future research