Despite the widespread adoption of Vision-Language Understanding (VLU)
benchmarks such as VQA v2, OKVQA, A-OKVQA, GQA, VCR, SWAG, and VisualCOMET, our
analysis reveals a pervasive issue affecting their integrity: these benchmarks
contain samples where answers rely on assumptions unsupported by the provided
context. Training models on such data foster biased learning and hallucinations
as models tend to make similar unwarranted assumptions. To address this issue,
we collect contextual data for each sample whenever available and train a
context selection module to facilitate evidence-based model predictions. Strong
improvements across multiple benchmarks demonstrate the effectiveness of our
approach. Further, we develop a general-purpose Context-AwaRe Abstention (CARA)
detector to identify samples lacking sufficient context and enhance model
accuracy by abstaining from responding if the required context is absent. CARA
exhibits generalization to new benchmarks it wasn't trained on, underscoring
its utility for future VLU benchmarks in detecting or cleaning samples with
inadequate context. Finally, we curate a Context Ambiguity and Sufficiency
Evaluation (CASE) set to benchmark the performance of insufficient context
detectors. Overall, our work represents a significant advancement in ensuring
that vision-language models generate trustworthy and evidence-based outputs in
complex real-world scenarios.

通过收集上下文数据并训练一个上下文选择模块来解决 VLU 基准测试中普遍存在的问题，防止模型依赖无法支持的假设进行训练，同时开发了一个通用的 CARA 检测器来增强模型准确性，并且通过创建 CASE 集评估不充分的上下文检测器的性能，这些工作显著推进了在复杂现实场景中生成可信可靠的视觉语言模型的发展。

检测缺乏上下文的多模态情况并避免无根据的预测

Detecting Multimodal Situations with Insufficient Context and Abstaining  from Baseless Predictions

In this paper, we study the classification problem in which we have access to
easily obtainable surrogate for true labels, namely complementary labels, which
specify classes that observations do \textbf{not} belong to. Let $Y$ and
$\bar{Y}$ be the true and complementary labels, respectively. We first model
the annotation of complementary labels via transition probabilities
$P(\bar{Y}=i|Y=j), i\neq j\in\{1,\cdots,c\}$, where $c$ is the number of
classes. Previous methods implicitly assume that $P(\bar{Y}=i|Y=j), \forall
i\neq j$, are identical, which is not true in practice because humans are
biased toward their own experience. For example, as shown in Figure 1, if an
annotator is more familiar with monkeys than prairie dogs when providing
complementary labels for meerkats, she is more likely to employ "monkey" as a
complementary label. We therefore reason that the transition probabilities will
be different. In this paper, we propose a framework that contributes three main
innovations to learning with \textbf{biased} complementary labels: (1) It
estimates transition probabilities with no bias. (2) It provides a general
method to modify traditional loss functions and extends standard deep neural
network classifiers to learn with biased complementary labels. (3) It
theoretically ensures that the classifier learned with complementary labels
converges to the optimal one learned with true labels. Comprehensive
experiments on several benchmark datasets validate the superiority of our
method to current state-of-the-art methods.

本文提出了一个框架，它使用没有偏见的转移概率估计，提供了一个通用的方法来修改传统的损失函数，并扩展标准的深度神经网络分类器，从而使其能够学习偏差的互补标签，并且确保了使用互补标签学习分类器收敛于使用真实标签学习的最优分类器。综合实验表明，本文方法优于当前最先进的方法。