The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors’ institution is not intended and should not be inferred. Role of the funders/sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study collection, management, analysis, and interpretation of data preparation, review, or approval of the manuscript or decision to submit the manuscript for publication.Īcknowledgments: The authors would like to thank the anonymous reviewers for their comments on prior versions of this manuscript. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.įunding: This work was supported by the National Natural Science Foundation of China (31960186 31760288 31660278 61967009). No authors reported any financial or other conflicts of interest in relation to the work described.Įthical principles: The authors affirm having followed professional ethical guidelines in preparing this work. The results indicate that (1) as a generalized and flexible model, the JVRT-LCDM realizes high correct classification rates and accurate speed parameter recovery (2) the JVRT-LCDM outperforms the existing models in terms of model-data fit, diagnostic consistency, and estimation of specific individuals in practical cognitive diagnosis assessments and (3) the JVRT-LCDM provides reliable evidence for nonconstant speed modeling.Ĭonflict of interest disclosures: Each author signed a form for disclosure of potential conflicts of interest. The feasibility of the JVRT-LCDM is investigated via a Monte Carlo simulation study using a Bayesian estimation scheme, and two empirical datasets are then analyzed to illustrate the applicability of the JVRT-LCDM in practice. Moreover, some existing models from psychometric literatures are included in the JVRT-LCDM as special cases. ![]() The JVRT-LCDM not only provides fine-grained diagnostic feedback without strict model constraints but also clarifies the specific speed trajectory of individuals. Typical to videos that are sped up uniformly.To advance the theoretical foundation of incorporating response times (RTs) into diagnostic classification models (DCMs), this study attempts to further derive, test and illustrate a generalized modeling framework (known as the JVRT-LCDM) that can simultaneously analyze response accuracy and differential speediness based on an existing method (Zhan et al., British Journal of Mathematical and Statistical Psychology, 71(2), 262–286, 2018). ![]() Viewers to watch videos faster, but with less of the jittery, unnatural motions SpeedNet for generating time-varying, adaptive video speedups, which can allow Recognition, and can be used for video retrieval. How those learned features can boost the performance of self-supervised action Space-time representation that goes beyond simple motion cues. Predicting the speed of videos, the model learns a powerful and meaningful Source video file can be uploaded from your computer or smartphone or fetched from another server by URL. Videos containing complex natural motions, and examine the visual cues it Upload and convert video to GIF With this online video converter you can upload your mp4, avi, WebM, flv, wmv and many other popular types of video and rich media files to turn them into high-quality animated GIFs. We demonstrate prediction results by SpeedNet on a wide range of We show how this single, binaryĬlassification network can be used to detect arbitrary rates of speediness of Without requiring any manual annotations. Is trained on a large corpus of natural videos in a self-supervised manner, ![]() To detect if a video is playing at normal rate, or if it is sped up. The core component in our approach is SpeedNet-a novel deep network trained ![]() Videos-whether they move faster, at, or slower than their "natural" speed. Freeman, Michael Rubinstein, Michal Irani, Tali Dekel Download PDF Abstract: We wish to automatically predict the "speediness" of moving objects in Authors: Sagie Benaim, Ariel Ephrat, Oran Lang, Inbar Mosseri, William T.
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