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Special Report, Opinion, Americas
#1
Thus, how to model the temporal information in a long-range video clip is a very critical problem. In this paper, our goal is to extend the DETR-like object detection into the video object detection domain. Our insights are four aspects. Firstly, we observe that the video clip contains rich inherent temporal information, e.g., rich visual cues of motion patterns. The whole video clip is like a sentence, and each frame contributes similarly to each word in natural language processing. Transformers can not only be used in inner each frame to model the interaction of each object but also be used to link objects along the temporal dimension. The learning process of DETR can be seen as the grouping process: grouping each object into an object query. Thus, these query embeddings can represent the instances of each frame and it is natural to link these sparse query embeddings via another temporal transformer.




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