When attempting to transfer learn for an image captioning task, we must use a source dataset for image captioning or visual question answering, since image classification by itself is not similar enough

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The question asks whether, when attempting to transfer learn for an image captioning task, we must use a source dataset for image captioning or visual question answering, since image classification by itself is not similar enough.
Option A: True. This statement argues that a source dataset for image captioning or VQA is required because image classification alone is not similar enough for transfer learning to captioning. A nuanced view would be that transfer learning effectiveness depends on the similarity between the source task and the target task. Image captioning and VQA are closer in modality and objective (generating or interpreting textual descriptions from images) than plain image classification, so using a related dataset can improve performance and provide useful feature representations. This option aligns with common practice in multimodal learning, where models benefit from pretraining on related tasks.
Option B: False. This claims that it is not necessary to use a source dataset related to captioning or VQA and that image classification alone would suffice or be sufficiently similar. In reality, while image classification pretraining can still offer useful features, the target task of image captioning or VQA often benefits from pretraining on tasks more aligned to language and multimodal reasoning. Hence, stating that image classification is not similar enough and a related source dataset is unnecessary would overlook the benefit of task similarity and cross-task transfer.
To evaluate the claim, one weighs the relationship between source and target tasks. If the source task is dissimilar (pure image classification with a fixed label space) and the target requires generating descriptive text or answering open-ended questions about an image, the transfer is typically less effective than using a more related source (captioning or VQA). Conversely, some shared visual features learned during classification can still be helpful, but the premise of needing a more closely related source is generally supported by empirical findings in transfer learning for multimodal tasks.
Therefore, the reasoning supports that using a related source dataset (captioning or VQA) is advantageous, while relying solely on image classification is less aligned with the target task. The distinction hinges on the degree of task similarity and the benefit of language-anchored or cross-modal representations in captioning and VQA contexts.

CS-7643-O01, OAN, OSZ Quiz #3: Convolutional Neural Network Architectures (Lesson 6), Visualization (Lesson 7), Advanced Computer Vision Architectures (Lesson 8)

When attempting to transfer learn for an image captioning task, we must use a source dataset for image captioning or visual question answering, since image classification by itself is not similar enough

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