222 research outputs found
Scene Text Synthesis for Efficient and Effective Deep Network Training
A large amount of annotated training images is critical for training accurate
and robust deep network models but the collection of a large amount of
annotated training images is often time-consuming and costly. Image synthesis
alleviates this constraint by generating annotated training images
automatically by machines which has attracted increasing interest in the recent
deep learning research. We develop an innovative image synthesis technique that
composes annotated training images by realistically embedding foreground
objects of interest (OOI) into background images. The proposed technique
consists of two key components that in principle boost the usefulness of the
synthesized images in deep network training. The first is context-aware
semantic coherence which ensures that the OOI are placed around semantically
coherent regions within the background image. The second is harmonious
appearance adaptation which ensures that the embedded OOI are agreeable to the
surrounding background from both geometry alignment and appearance realism. The
proposed technique has been evaluated over two related but very different
computer vision challenges, namely, scene text detection and scene text
recognition. Experiments over a number of public datasets demonstrate the
effectiveness of our proposed image synthesis technique - the use of our
synthesized images in deep network training is capable of achieving similar or
even better scene text detection and scene text recognition performance as
compared with using real images.Comment: 8 pages, 5 figure
Cooperative Transmission for Underwater Acoustic Communications
Underwater acoustic channels normally have low data rate, long propagation delay, severe multipath effect, and time varying fading. Cooperative transmission is a new wireless communication technique in which diversity gain is achieved by utilizing relay nodes as virtual antennae. In this paper, we investigate cooperative transmission techniques for underwater acoustic communications. First, we study the performance of several cooperative transmission schemes, originally designed for radio communications, in an underwater scenario. Second, by taking advantage of the low propagation speed of sound, we design a new wave cooperative transmission scheme. In this scheme, the relay nodes amplify the signal received from the source node, and then forward the signal immediately to the destination. The goal is to alter the multipath effect at the receiver. Third, we derive the performance upper bound for the proposed wave cooperative transmission scheme. The simulation results show that the proposed wave cooperative transmission has significant advantages over the traditional direct transmission and the existing cooperative transmission schemes originally designed for radio wireless networks. ©2008 IEEE
Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition
Self-supervised learning (SSL) for WiFi-based human activity recognition (HAR) holds great promise due to its ability to address the challenge of insufficient labeled data. However, directly transplanting SSL algorithms, especially contrastive learning, originally designed for other domains to CSI data, often fails to achieve the expected performance. We attribute this issue to the inappropriate alignment criteria, which disrupt the semantic distance consistency between the feature space and the input space. To address this challenge, we introduce \textbf{A}ntenna \textbf{R}esponse \textbf{C}onsistency (ARC) as a solution to define proper alignment criteria. ARC is designed to retain semantic information from the input space while introducing robustness to real-world noise. Moreover, we substantiate the effectiveness of ARC through a comprehensive set of experiments, demonstrating its capability to enhance the performance of self-supervised learning for WiFi-based HAR by achieving an increase of over 5\% in accuracy in most cases and achieving a best accuracy of 94.97\%
Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition
Self-supervised learning (SSL) for WiFi-based human activity recognition
(HAR) holds great promise due to its ability to address the challenge of
insufficient labeled data. However, directly transplanting SSL algorithms,
especially contrastive learning, originally designed for other domains to CSI
data, often fails to achieve the expected performance. We attribute this issue
to the inappropriate alignment criteria, which disrupt the semantic distance
consistency between the feature space and the input space. To address this
challenge, we introduce \textbf{A}ntenna \textbf{R}esponse \textbf{C}onsistency
(ARC) as a solution to define proper alignment criteria. ARC is designed to
retain semantic information from the input space while introducing robustness
to real-world noise. Moreover, we substantiate the effectiveness of ARC through
a comprehensive set of experiments, demonstrating its capability to enhance the
performance of self-supervised learning for WiFi-based HAR by achieving an
increase of over 5\% in accuracy in most cases and achieving a best accuracy of
94.97\%
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