193 research outputs found
Optical Flow Requires Multiple Strategies (but only one network)
We show that the matching problem that underlies optical flow requires
multiple strategies, depending on the amount of image motion and other factors.
We then study the implications of this observation on training a deep neural
network for representing image patches in the context of descriptor based
optical flow. We propose a metric learning method, which selects suitable
negative samples based on the nature of the true match. This type of training
produces a network that displays multiple strategies depending on the input and
leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow
benchmarks
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
Techniques for dense semantic correspondence have provided limited ability to
deal with the geometric variations that commonly exist between semantically
similar images. While variations due to scale and rotation have been examined,
there lack practical solutions for more complex deformations such as affine
transformations because of the tremendous size of the associated solution
space. To address this problem, we present a discrete-continuous transformation
matching (DCTM) framework where dense affine transformation fields are inferred
through a discrete label optimization in which the labels are iteratively
updated via continuous regularization. In this way, our approach draws
solutions from the continuous space of affine transformations in a manner that
can be computed efficiently through constant-time edge-aware filtering and a
proposed affine-varying CNN-based descriptor. Experimental results show that
this model outperforms the state-of-the-art methods for dense semantic
correspondence on various benchmarks
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