情感视觉对话：基于视觉基础对话的情感推理大规模基准
Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations
AI总结：
本文提出情感视觉对话任务及包含50K个10轮视觉对话的AffectVisDial数据集，覆盖问答、情感预测和情感解释生成，并验证了基线模型的情感推理能力。
摘要：
我们提出情感视觉对话任务，这是一个情感解释与推理任务，作为研究视觉基础对话中情感形成机制的测试平台。该任务包含三项技能：（1）基于对话的问答，（2）基于对话的情感预测，以及（3）基于对话的情感解释生成。我们的核心贡献是收集了一个大规模数据集，命名为AffectVisDial，包含50K个10轮视觉基础对话，以及对话结束时的情感归因和基于对话的文本情感解释，总计耗费27,180个工作小时。我们解释了收集该数据集时的设计决策，并介绍了与对话参与者相关的问题者与回答者任务。我们训练并展示了基于最先进模型改编的可靠情感视觉对话基线。值得注意的是，我们的模型生成的回答在面对视觉基础对话时展现出有前景的情感推理能力。我们的项目页面可在https://affective-visual-dialog.github.io访问。
英文摘要：
We introduce Affective Visual Dialog, an emotion explanation and reasoning task as a testbed for research on understanding the formation of emotions in visually grounded conversations. The task involves three skills: (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction and (3) Affective emotion explanation generation based on the dialog. Our key contribution is the collection of a large-scale dataset, dubbed AffectVisDial, consisting of 50K 10-turn visually grounded dialogs as well as concluding emotion attributions and dialog-informed textual emotion explanations, resulting in a total of 27,180 working hours. We explain our design decisions in collecting the dataset and introduce the questioner and answerer tasks that are associated with the participants in the conversation. We train and demonstrate solid Affective Visual Dialog baselines adapted from state-of-the-art models. Remarkably, the responses generated by our models show promising emotional reasoning abilities in response to visually grounded conversations. Our project page is available at https://affective-visual-dialog.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
