ExamworthyExamworthy
Open the appStart free

NCA-GENM - Multimodal Data - Section 3.4

Apply early, late, and intermediate model fusion techniques.

Compare early fusion, which concatenates raw inputs before encoding, with late fusion, which merges independent modality outputs, and intermediate fusion, which combines features mid-network. Choose the appropriate model fusion technique based on the alignment of modalities and the latency constraints of the application.

Model fusion

Practice question for this objective

Free sampleMultimodal Datahard

In a multimodal architecture, what fundamentally distinguishes an early fusion design from a late fusion design?

  • AEarly fusion combines the modalities at the input or low-level feature stage so a single joint model learns over the merged representation, whereas late fusion produces a separate prediction per modality and combines those predictions at the decision stage. Correct
  • BEarly fusion trains each modality with its own loss function, whereas late fusion shares a single loss across all modalities from the first layer.
  • CEarly fusion always uses attention to combine modalities, whereas late fusion always uses simple concatenation of raw inputs.
  • DEarly fusion can only be applied to two modalities at once, whereas late fusion is the only approach that scales to three or more modalities.
Distinguish early fusion from late fusion by the stage at which modalities are combined and how the model is structured. The early-versus-late distinction is defined by where the modalities meet in the pipeline: early fusion merges low-level features so one joint model learns over them, while late fusion trains per-modality models and combines their outputs only at the decision stage.

Why A is correct: Correct. The defining axis is the stage at which information from different modalities meets: early fusion merges before or near the input so one model jointly encodes everything, while late fusion keeps modality-specific models that each emit a prediction, fused only at the output.

Why B is wrong: This inverts the relationship. Late fusion is the design that keeps per-modality branches, and those branches may each carry their own objective, while early fusion shares one joint objective. The statement is tempting because it correctly senses that one design has more independence, but it assigns that independence to the wrong approach.

Why C is wrong: Neither mechanism is mandated by either approach: early fusion classically concatenates raw or low-level features, and late fusion combines high-level outputs by averaging, voting, or a small meta-model. The claim is plausible because attention is a common fusion mechanism, but it wrongly fixes one mechanism to each design and reverses where concatenation typically appears.

Why D is wrong: Both approaches extend to any number of modalities: early fusion can concatenate three or more feature vectors and late fusion can combine three or more predictions. The statement is tempting if one assumes concatenation grows unwieldy, but there is no two-modality ceiling on early fusion, so the distinction is fabricated.

See more NCA-GENM practice questions, answers explained.

More in this domain

Back to all Multimodal Data objectives, or the NCA-GENM cert hub.

Examworthy is not affiliated with or endorsed by NVIDIA. Original, blueprint-aligned practice material only.