50 research outputs found
Exploring Object Relation in Mean Teacher for Cross-Domain Detection
Rendering synthetic data (e.g., 3D CAD-rendered images) to generate
annotations for learning deep models in vision tasks has attracted increasing
attention in recent years. However, simply applying the models learnt on
synthetic images may lead to high generalization error on real images due to
domain shift. To address this issue, recent progress in cross-domain
recognition has featured the Mean Teacher, which directly simulates
unsupervised domain adaptation as semi-supervised learning. The domain gap is
thus naturally bridged with consistency regularization in a teacher-student
scheme. In this work, we advance this Mean Teacher paradigm to be applicable
for cross-domain detection. Specifically, we present Mean Teacher with Object
Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster
R-CNN by integrating the object relations into the measure of consistency cost
between teacher and student modules. Technically, MTOR firstly learns
relational graphs that capture similarities between pairs of regions for
teacher and student respectively. The whole architecture is then optimized with
three consistency regularizations: 1) region-level consistency to align the
region-level predictions between teacher and student, 2) inter-graph
consistency for matching the graph structures between teacher and student, and
3) intra-graph consistency to enhance the similarity between regions of same
class within the graph of student. Extensive experiments are conducted on the
transfers across Cityscapes, Foggy Cityscapes, and SIM10k, and superior results
are reported when comparing to state-of-the-art approaches. More remarkably, we
obtain a new record of single model: 22.8% of mAP on Syn2Real detection
dataset.Comment: CVPR 2019; The codes and model of our MTOR are publicly available at:
https://github.com/caiqi/mean-teacher-cross-domain-detectio
Style Separation and Synthesis via Generative Adversarial Networks
Style synthesis attracts great interests recently, while few works focus on
its dual problem "style separation". In this paper, we propose the Style
Separation and Synthesis Generative Adversarial Network (S3-GAN) to
simultaneously implement style separation and style synthesis on object
photographs of specific categories. Based on the assumption that the object
photographs lie on a manifold, and the contents and styles are independent, we
employ S3-GAN to build mappings between the manifold and a latent vector space
for separating and synthesizing the contents and styles. The S3-GAN consists of
an encoder network, a generator network, and an adversarial network. The
encoder network performs style separation by mapping an object photograph to a
latent vector. Two halves of the latent vector represent the content and style,
respectively. The generator network performs style synthesis by taking a
concatenated vector as input. The concatenated vector contains the style half
vector of the style target image and the content half vector of the content
target image. Once obtaining the images from the generator network, an
adversarial network is imposed to generate more photo-realistic images.
Experiments on CelebA and UT Zappos 50K datasets demonstrate that the S3-GAN
has the capacity of style separation and synthesis simultaneously, and could
capture various styles in a single model
Unsupervised Scene Adaptation with Memory Regularization in vivo
We consider the unsupervised scene adaptation problem of learning from both
labeled source data and unlabeled target data. Existing methods focus on
minoring the inter-domain gap between the source and target domains. However,
the intra-domain knowledge and inherent uncertainty learned by the network are
under-explored. In this paper, we propose an orthogonal method, called memory
regularization in vivo to exploit the intra-domain knowledge and regularize the
model training. Specifically, we refer to the segmentation model itself as the
memory module, and minor the discrepancy of the two classifiers, i.e., the
primary classifier and the auxiliary classifier, to reduce the prediction
inconsistency. Without extra parameters, the proposed method is complementary
to the most existing domain adaptation methods and could generally improve the
performance of existing methods. Albeit simple, we verify the effectiveness of
memory regularization on two synthetic-to-real benchmarks: GTA5 -> Cityscapes
and SYNTHIA -> Cityscapes, yielding +11.1% and +11.3% mIoU improvement over the
baseline model, respectively. Besides, a similar +12.0% mIoU improvement is
observed on the cross-city benchmark: Cityscapes -> Oxford RobotCar.Comment: 7 pages, 4 figures, 6 table