We characterise some of the quirks and shortcomings in the exploration of Visual Dialogue (VD) - a sequential question-answering task where the questions and corresponding answers are related through given visual stimuli. To do so, we develop an embarrassingly simple method based on Canonical Correlation Analysis (CCA) that, on the standard dataset, achieves near state-of-the-art performance for some standard metric. In direct contrast to current complex and over-parametrised architectures that are both compute and time intensive, our method ignores the visual stimuli, ignores the sequencing of dialogue, does not need gradients, uses off-the-shelf feature extractors, has at least an order of magnitude fewer parameters, and learns in practically no time. We argue that these results are indicative of issues in current approaches to Visual Dialogue relating particularly to implicit dataset biases, under-constrained task objectives, and over-constrained evaluation metrics, and consequently, discuss some avenues to ameliorate these issues.

使用基于规范相关分析(CCA)的简单方法，我们对视觉对话进行了特征描述，并指出现有方法存在的问题。该方法忽略了视觉刺激和对话排序，不需要梯度，具有较少的参数数量和学习时间，并在标准数据集上达到了接近最先进的性能水平，进一步分析了数据集的隐含偏差和过度约束的评估指标效应。

没有视觉或对话的视觉对话