64 research outputs found
Share Beijing Stories in the Context of Media Convergence
International Exchange Center is one of the “four centers” of Beijing’s strategic positioning. Accompanied by the continuous progress and development of media, Beijing’s city image is subject to increasingly frequent exchanges among cities of different countries, and the requirements for external communication are also increasing. How to effectively promote Beijing’s external communication in the cross-cultural context and share a good “Beijing city story” of the new era? One of the most important ways is to effectively shape Beijing’s city image through the media, to improve Beijing’s influence through the media, to adjust the way of Beijing’s international communication in a timely manner, and to create communication contents that are closer to the audience’s psychology, so as to achieve a better effect of the external communication of Beijing’s city image
Towards Black-box Adversarial Example Detection: A Data Reconstruction-based Method
Adversarial example detection is known to be an effective adversarial defense
method. Black-box attack, which is a more realistic threat and has led to
various black-box adversarial training-based defense methods, however, does not
attract considerable attention in adversarial example detection. In this paper,
we fill this gap by positioning the problem of black-box adversarial example
detection (BAD). Data analysis under the introduced BAD settings demonstrates
(1) the incapability of existing detectors in addressing the black-box scenario
and (2) the potential of exploring BAD solutions from a data perspective. To
tackle the BAD problem, we propose a data reconstruction-based adversarial
example detection method. Specifically, we use variational auto-encoder (VAE)
to capture both pixel and frequency representations of normal examples. Then we
use reconstruction error to detect adversarial examples. Compared with existing
detection methods, the proposed method achieves substantially better detection
performance in BAD, which helps promote the deployment of adversarial example
detection-based defense solutions in real-world models.Comment: 14 pages, 8 figures, 13 table
Delving into Crispness: Guided Label Refinement for Crisp Edge Detection
Learning-based edge detection usually suffers from predicting thick edges.
Through extensive quantitative study with a new edge crispness measure, we find
that noisy human-labeled edges are the main cause of thick predictions. Based
on this observation, we advocate that more attention should be paid on label
quality than on model design to achieve crisp edge detection. To this end, we
propose an effective Canny-guided refinement of human-labeled edges whose
result can be used to train crisp edge detectors. Essentially, it seeks for a
subset of over-detected Canny edges that best align human labels. We show that
several existing edge detectors can be turned into a crisp edge detector
through training on our refined edge maps. Experiments demonstrate that deep
models trained with refined edges achieve significant performance boost of
crispness from 17.4% to 30.6%. With the PiDiNet backbone, our method improves
ODS and OIS by 12.2% and 12.6% on the Multicue dataset, respectively, without
relying on non-maximal suppression. We further conduct experiments and show the
superiority of our crisp edge detection for optical flow estimation and image
segmentation.Comment: Accepted by TI
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