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  1. ; ; ; ; ; ( , WWW Conference)
    Free, publicly-accessible full text available April 1, 2023
  2. ; ; ; ; ; ; ; ( , WWW conference)
    Free, publicly-accessible full text available April 1, 2023
  3. ; ; ; ; ; ; ( , IEEE Big Data)
    Free, publicly-accessible full text available December 1, 2022
  4. ; ; ; ; ; ; ( , ACM transactions on sensor networks)
    Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR withmore »imperceptible system latency. CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.« less
    Free, publicly-accessible full text available February 1, 2023
  5. ; ; ; ; ; ( , IEEE Big Data)
    Free, publicly-accessible full text available December 1, 2022
  6. ; ; ; ; ( , EMNLP)
    Free, publicly-accessible full text available November 1, 2022
  7. ; ; ; ; ; ( , 2nd ACM International Conference on AI in Finance)
    Free, publicly-accessible full text available November 3, 2022
  8. ; ; ; ; ; ( , ACM CIKM)
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  9. ; ; ; ; ; ( , ACM transactions on intelligent systems and technology)
    Free, publicly-accessible full text available October 1, 2022
  10. ; ; ; ; ; ; ; ; ( , EMNLP)
    Free, publicly-accessible full text available November 1, 2022