Designing a Visual Question Answering system
multimodal architecture design.
encode the image with a vision backbone, encode the question with a text encoder, fuse them via cross-attention into a joint representation, then decode or classify the answer.
WHAT THIS TESTS The interviewer wants an end-to-end multimodal design that explicitly fuses vision and language, showing you know fusion is the crux of VQA.
A GOOD ANSWER COVERS Three stages: encode, fuse, answer. For the image, use a vision backbone, historically a CNN with region features from an object detector, now usually a Vision Transformer producing patch embeddings, to get a set of visual features. For the question, use a text encoder such as a transformer to get contextual token embeddings. The fusion stage is central: cross-attention or co-attention lets the question tokens attend over image regions and vice versa, building a joint representation that grounds words in pixels. Finally, answer generation has two common forms. Closed VQA treats it as classification over a fixed vocabulary of frequent answers, which is simple and accurate on benchmarks. Open VQA uses a language decoder to generate free-form answers, which is more flexible and matches modern vision-language models. Modern systems often replace the bespoke fusion with a projection layer feeding image features into an LLM.
COMMON WRONG ANSWERS Encoding image and text separately and concatenating only the final vectors with no attention, which loses the grounding needed to answer region-specific questions. Another error is always framing VQA as classification, which fails on novel or compositional answers.
LIKELY FOLLOW-UPS Expect questions on co-attention versus simple concatenation, on classification versus generation trade-offs, on handling counting and reading text in images, and on how LLaVA-style models connect a frozen vision encoder to an LLM.
ONE CONCRETE EXAMPLE Given a kitchen photo and the question how many apples are on the table, the vision encoder produces region features, the text encoder embeds the question, cross-attention focuses the word apples on the fruit bowl region, and a classifier or decoder outputs the count, demonstrating grounded multimodal reasoning rather than blind guessing.
Read the original → arxiv.org
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