Exam NCA-GENM Topic 1 Question 136 Discussion

Actual exam question for NVIDIA's NCA-GENM exam
Question #: 136
Topic #: 1

You are working on a multimodal sentiment analysis task where you have both textual reviews and corresponding product images. You want to build an attention mechanism to identify the most relevant parts of the image that contribute to the sentiment expressed in the text. Which of the following attention mechanisms is BEST suited for generating spatial attention maps highlighting these relevant regions in the image?

A. Self-attention in the text encoder (e.g., Transformer). B. Channel attention in the image encoder (e.g., Squeeze-and-Excitation). C. Spatial attention in the image encoder, conditioned on the text embedding (e.g., attention over image features based on text query). D. Temporal attention in a video encoder. E. Global average pooling of image features.

Suggested Answer: C Vote an answer

Spatial attention, conditioned on the text embedding, directly addresses the task. This mechanism allows the model to focus on specific regions of the image that are most relevant to the sentiment expressed in the text. The text embedding acts as a 'query' to attend over the image features, generating a spatial attention map that highlights the contributing regions. Self attention in text (A) focuses on relationships within the text itself. Channel attention (B) focuses on feature channel importance, not spatial localization related to the text. Temporal attention (D) is irrelevant for static images. Global average pooling (E) loses spatial information.

by Ford at Nov 11, 2025, 01:08 AM

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