NCA-GENM Exam Dumps | You are working with a multimodal model that combines text and video data for action recognition. The

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Question 132/192

You are working with a multimodal model that combines text and video data for action recognition. The text data consists of descriptions of the actions, and the video data consists of sequences of frames. You want to fuse these modalities at a late fusion stage. Which of the following approaches BEST describes late fusion?

A. Concatenating the raw pixel values of video frames with the word embeddings of the text descriptions.

B. Training separate models for text and video data and averaging their predictions.

C. Training a single model with both text and video data as input and using a shared embedding space.

D. Training separate models for text and video data and concatenating their learned feature representations before feeding them into a final classifier.

E. Applying attention mechanisms to weigh different parts of the text and video data before feeding them into a shared model.

Correct Answer: D

Late fusion involves processing each modality separately to obtain feature representations and then combining these representations at a later stage, typically by concatenation or averaging, before making a final prediction. Averaging predictions (option B) is a specific type of late fusion. Concatenating raw pixel values and word embeddings (option A) is an example of early fusion. Training a single model with a shared embedding space (option C) is also closer to early or intermediate fusion. Attention mechanisms can be used in various fusion strategies but do not define late fusion specifically.

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