Image-Based Video Retrieval Using Deep Feature

Wang, Mao; Ming, Yuewei; Liu, Qiang; Yin, Jianping

doi:10.1109/smartcomp.2017.7947017

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…Improving the pre-trained model's ability especially for generation can be further explored. What's more, in addition to the downstream tasks we listed above, other downstream tasks such as multi-modal sentiment analysis (Zadeh et al 2017), imagebased retrieval (Wang et al 2017) have also been explored recently.…”

Section: Actionmentioning

confidence: 99%

Survey: Transformer based Video-Language Pre-training

Ruan¹,

Jin²

2021

Preprint

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Inspired by the success of transformer-based pre-training methods on natural language tasks and further computer vision tasks, researchers have begun to apply transformer to video processing. This survey aims to give a comprehensive overview on transformer-based pre-training methods for Video-Language learning. We first briefly introduce the transformer structure as the background knowledge, including attention mechanism, position encoding etc. We then describe the typical paradigm of pretraining & fine-tuning on Video-Language processing in terms of proxy tasks, downstream tasks and commonly used video datasets. Next, we categorize transformer models into Single-Stream and Multi-Stream structures, highlight their innovations and compare their performances. Finally, we analyze and discuss the current challenges and possible future research directions for Video-Language pre-training.

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Section: Actionmentioning

confidence: 99%