17,164 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
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
This paper takes a step towards temporal reasoning in a dynamically changing
video, not in the pixel space that constitutes its frames, but in a latent
space that describes the non-linear dynamics of the objects in its world. We
introduce the Kalman variational auto-encoder, a framework for unsupervised
learning of sequential data that disentangles two latent representations: an
object's representation, coming from a recognition model, and a latent state
describing its dynamics. As a result, the evolution of the world can be
imagined and missing data imputed, both without the need to generate high
dimensional frames at each time step. The model is trained end-to-end on videos
of a variety of simulated physical systems, and outperforms competing methods
in generative and missing data imputation tasks.Comment: NIPS 201
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.Comment: Accepted by Journal of Structural Biolog
- …