Papers with CLEVR-Dialog
Neuro-Symbolic Visual Dialog (2022.coling-1)
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| Challenge: | Existing methods for visual dialog require large amounts of training data, which is prohibitive for most settings. |
| Approach: | They propose a method that integrates deep learning and symbolic program execution for multi-round visual reasoning. |
| Outcome: | The proposed model outperforms existing methods on long-distance co-reference resolution and vanishing question-answering performance. |
CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog (N19-1)
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| Challenge: | Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image. |
| Approach: | They construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset and use it to benchmark performance of standard visual dialog models. |
| Outcome: | The proposed model is based on a large diagnostic dataset for studying multi-round reasoning in visual dialog. |