4,433 research outputs found
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by
considering that a network is trained on multiple tasks, but performs one task
at a time, an approach we refer to as "single-tasking multiple tasks". The
network thus modifies its behaviour through task-dependent feature adaptation,
or task attention. This gives the network the ability to accentuate the
features that are adapted to a task, while shunning irrelevant ones. We further
reduce task interference by forcing the task gradients to be statistically
indistinguishable through adversarial training, ensuring that the common
backbone architecture serving all tasks is not dominated by any of the
task-specific gradients. Results in three multi-task dense labelling problems
consistently show: (i) a large reduction in the number of parameters while
preserving, or even improving performance and (ii) a smooth trade-off between
computation and multi-task accuracy. We provide our system's code and
pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.Comment: CVPR 2019 Camera Read
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CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training
Recent image inpainting methods have made great progress but often struggle
to generate plausible image structures when dealing with large holes in complex
images. This is partially due to the lack of effective network structures that
can capture both the long-range dependency and high-level semantics of an
image. To address these problems, we propose cascaded modulation GAN (CM-GAN),
a new network design consisting of an encoder with Fourier convolution blocks
that extract multi-scale feature representations from the input image with
holes and a StyleGAN-like decoder with a novel cascaded global-spatial
modulation block at each scale level. In each decoder block, global modulation
is first applied to perform coarse semantic-aware structure synthesis, then
spatial modulation is applied on the output of global modulation to further
adjust the feature map in a spatially adaptive fashion. In addition, we design
an object-aware training scheme to prevent the network from hallucinating new
objects inside holes, fulfilling the needs of object removal tasks in
real-world scenarios. Extensive experiments are conducted to show that our
method significantly outperforms existing methods in both quantitative and
qualitative evaluation.Comment: 32 pages, 18 figure
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