Americas, Special Report, Tech

AAjbdzt
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Americas, Special Report, Tech

Сообщение AAjbdzt » Чт июн 16, 2022 9:11 am

This boils down to using only the visual information in the temporal grounding. Surprisingly, the performance under such a setting is comparable to many models that normally read both queries and videos during testing. It also significantly outstrips random guess. To make the point more clear, let us provide some concrete empirical observations. We have identified two sorts of dominant biases that a model can exploit for over-fitting the training scenarios, namely the visual content bias and temporal interval bias. In detail, a few visual concepts and temporal intervals are more frequently queried by the sentence than others in the training dataset. Although the issue of dominant biases have been previously reported as language bias in visual-question answering tasks Ramakrishnan et al. 2018); Cadene et al. 2019); Chen et al. 2020), the preference of visual contents and temporal intervals is specific to the temporal grounding task and our report here is the first. For instance, the concept “run” is largely frequent than “sit” in the benchmark of ActivityNet.




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willinator
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Re: Americas, Special Report, Tech

Сообщение willinator » Вс янв 21, 2024 3:08 am



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