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An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance.

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Learning from Weakly-Labeled Web Videos via Exploring Sub-concepts., , , , , , , and . AAAI, page 1341-1349. AAAI Press, (2022)Understanding Traffic Density from Large-Scale Web Camera Data., , , and . CVPR, page 4264-4273. IEEE Computer Society, (2017)TDT: Teaching Detectors to Track without Fully Annotated Videos., , , and . CVPR Workshops, page 3939-3949. IEEE, (2022)Multiple Source Domain Adaptation with Adversarial Learning., , , , , and . ICLR (Workshop), OpenReview.net, (2018)The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift., , , , , , , , , and . CVPR, page 21262-21275. IEEE, (2022)An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance., , , , , , , , , and 2 other author(s). SEC, page 14:1-14:14. ACM, (2017)Multiple Source Domain Adaptation with Adversarial Training of Neural Networks., , , , , and . CoRR, (2017)Long Term Temporal Context for Per-Camera Object Detection., , , , and . CoRR, (2019)Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection., , , , and . CVPR, page 13072-13082. Computer Vision Foundation / IEEE, (2020)Adversarial Multiple Source Domain Adaptation., , , , , and . NeurIPS, page 8568-8579. (2018)