Browsing by Author "Li, J."

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    The hare and the tortoise: do earlier adopters of online channels purchase more?
    (Elsevier, 2015-06) Li, J.; Konuş, U.; Pauwels, Koen Hendrik; Business Administration; PAUWELS, Koen Hendrik
    Earlier adopters of a product or service tend to be more valuable than later adopters. Does this empirical generalization equally apply to earlier adopters of a multichannel retailer's new online channel too? This study segments customers on the basis of their responses to a new online channel and investigates the effects of their online channel adoption on purchase volumes across segments. The data cover 12.5 years of purchase history and individual transactions at a large multichannel French retailer of natural health products. Contrary to conventional wisdom, it is not innovators or early adopters, but rather the late majority segment that purchases more than the other segments, both before and after online adoption. Adoption of the firm's new online channel does not influence purchase volumes of heavy shopper segments (late majority and innovators), whereas light shopper segments tend to increase their purchases after adopting this new channel.
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    VisDrone-MOT2021: The vision meets drone multiple object tracking challenge results
    (IEEE, 2021) Chen, G.; Wang, W.; He, Z.; Wang, L.; Yuan, Y.; Zhang, D.; Zhang, J.; Zhu, P.; Gool, L. V.; Han, J.; Hoi, S.; Hu, Q.; Liu, M.; Sciarrone, A.; Sun, C.; Garibotto, C.; Tran, D. N. N.; Lavagetto, F.; Haleem, H.; Motorcu, Hakkı; Ateş, H. F.; Jeon, H. J.; Bisio, I.; Jeon, J. W.; Li, J.; Pham, J. H.; Jeon, M.; Feng, Q.; Li, S.; Tran, T. H. P.; Pan, X.; Song, Y. M.; Yao, Y.; Du, Y.; Xu, Z.; Luo, Z.; Motorcu, Hakkı
    Vision Meets Drone: Multiple Object Tracking (VisDrone-MOT2021) challenge - the forth annual activity organized by the VisDrone team - focuses on benchmarking UAV MOT algorithms in realistic challenging environments. It is held in conjunction with ICCV 2021. VisDrone-MOT2021 contains 96 video sequences in total, including 56 sequences (~24K frames) for training, 7 sequences (~3K frames) for validation and 33 sequences (~13K frames) for testing. Bounding-box annotations for novel object categories are provided every frame and temporally consistent instance IDs are also given. Additionally, occlusion ratio and truncation ratio are provided as extra useful annotations. The results of eight state-of-the-art MOT algorithms are reported and discussed. We hope that our VisDrone-MOT2021 challenge will facilitate future research and applications in the field of UAV vision.