Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Simple and Transferable Recognition-Aware Image Processing   Recent progress in image recognition has stimulated the deployment of vision
systems at an unprecedented scale. As a result, visual data are now often
consumed not only by humans but also by machines. Existing image processing
methods only optimize for better human perception, yet the resulting images may
not be accurately recognized by machines. This can be undesirable, e.g., the
images can be improperly handled by search engines or recommendation systems.
In this work, we examine simple approaches to improve machine recognition of
processed images: optimizing the recognition loss directly on the image
processing network or through an intermediate transforming model.
Interestingly, the processing model's ability to enhance recognition quality
can transfer when evaluated on models of different architectures, recognized
categories, tasks and training datasets. This makes the methods applicable even
when we do not have the knowledge of future recognition models, e.g., if we
upload processed images to the Internet. We conduct experiments on multiple
image processing tasks, with ImageNet classification and PASCAL VOC detection
as recognition tasks. With these simple yet effective methods, substantial
accuracy gain can be achieved with strong transferability and minimal image
quality loss. Through a user study we further show that the accuracy gain can
transfer to a black-box cloud model. Finally, we try to explain this
transferability phenomenon by demonstrating the similarities of different
models' decision boundaries. Code is available at
https://github.com/liuzhuang13/Transferable_RA .

14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys