论文标题
ECCV标题：通过收集MS-Coco的机器和人类验证的图像捕获关联来纠正假否定性
ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO
论文作者
论文摘要
图像文本匹配（ITM）是评估视觉和语言质量（VL）模型的常见任务。但是，现有的ITM基准有一个重大限制。他们有许多缺失的信件，源自数据构建过程本身。例如，标题仅与一个图像匹配，尽管标题可以与其他类似图像匹配，反之亦然。为了纠正大规模的假否定因素，我们通过提供与机器和人类注释者的缺失关联来构建扩展的可可验证（ECCV）标题数据集。我们在注释过程中采用五个具有不同属性的最先进的ITM模型。与原始的MS-Coco相比，我们的数据集提供了X3.6正面图像到支撑关联和X8.5字幕 - 图像关联。我们还建议使用基于等级的公制映射@r，而不是流行的召回@k（r@k）。我们在现有和拟议的基准测试中重新评估了现有的25个VL模型。我们的发现是现有基准，例如可可1K r@k，可可5k r@k，cxc r@1彼此高度相关，而当我们转移到ECCV MAP@r时，排名会改变。最后，我们深入研究机器注释者选择引入的偏差的效果。源代码和数据集可从https://github.com/naver-ai/eccv-caption获得
Image-Text matching (ITM) is a common task for evaluating the quality of Vision and Language (VL) models. However, existing ITM benchmarks have a significant limitation. They have many missing correspondences, originating from the data construction process itself. For example, a caption is only matched with one image although the caption can be matched with other similar images and vice versa. To correct the massive false negatives, we construct the Extended COCO Validation (ECCV) Caption dataset by supplying the missing associations with machine and human annotators. We employ five state-of-the-art ITM models with diverse properties for our annotation process. Our dataset provides x3.6 positive image-to-caption associations and x8.5 caption-to-image associations compared to the original MS-COCO. We also propose to use an informative ranking-based metric mAP@R, rather than the popular Recall@K (R@K). We re-evaluate the existing 25 VL models on existing and proposed benchmarks. Our findings are that the existing benchmarks, such as COCO 1K R@K, COCO 5K R@K, CxC R@1 are highly correlated with each other, while the rankings change when we shift to the ECCV mAP@R. Lastly, we delve into the effect of the bias introduced by the choice of machine annotator. Source code and dataset are available at https://github.com/naver-ai/eccv-caption
