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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
This paper introduces a large-scale, multi-label and multitask video dataset named Scenes-Objects-Actions (SOA). Most prior video datasets are based on a predefined taxonomy, which is used to define the keyword queries issued to search engines. The videos retrieved by the search engines are then verified for correctness by human annotators. Datasets collected in this manner tend to generate high classification accuracy as search engines typically rank "easy" videos first. The SOA dataset adopts<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-01264-9_39">doi:10.1007/978-3-030-01264-9_39</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rny2zdol7vcndl7ofypkfgxpx4">fatcat:rny2zdol7vcndl7ofypkfgxpx4</a> </span>
more »... a different approach. We rely on uniform sampling to get a better representation of videos on the Web. Trained annotators are asked to provide free-form text labels describing each video in three different aspects: scene, object and action. These raw labels are then merged, split and renamed to generate a taxonomy for SOA. All the annotations are verified again based on the taxonomy. The final dataset includes 562K videos with 3.64M annotations spanning 49 categories for scenes, 356 for objects, 148 for actions, and naturally captures the long tail distribution of visual concepts in the real world. We show that datasets collected in this way are quite challenging by evaluating existing popular video models on SOA. We provide in-depth analysis about the performance of different models on SOA, and highlight potential new directions in video classification. We compare SOA with existing datasets and discuss various factors that impact the performance of transfer learning. A keyfeature of SOA is that it enables the empirical study of correlation among scene, object and action recognition in video. We present results of this study and further analyze the potential of using the information learned from one task to improve the others. We also demonstrate different ways of scaling up SOA to learn better features. We believe that the challenges presented by SOA offer the opportunity for further advancement in video analysis as we progress from single-label classification towards a more comprehensive understanding of video data.
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