Examining Cooperation in Visual Dialog Models
원문 초록
In this work we propose a blackbox intervention method for visual dialog models, with the aim of assessing the contribution of individual linguistic or visual components. Concretely, we conduct structured or randomized interventions that aim to impair an individual component of the model, and observe changes in task performance. We reproduce a state-of-the-art visual dialog model and demonstrate that our methodology yields surprising insights, namely that both dialog and image information have minimal contributions to task performance. The intervention method presented here can be applied as a sanity check for the strength and robustness of each component in visual dialog systems.
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Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Collecting Visually-Grounded Dialogue with A Game Of Sorts
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts
Unified Multimodal Model with Unlikelihood Training for Visual Dialog
LAVIS: A Library for Language-Vision Intelligence
토론
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