VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models

• URL: http://arxiv.org/abs/2205.15237v1
• Date: Mon, 30 May 2022 16:52:30 GMT
• Title: VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models
• Authors: Wangchunshu Zhou, Yan Zeng, Shizhe Diao, Xinsong Zhang
• Abstract summary: Recent advances in vision-language pre-training have demonstrated impressive performance in a range of vision-language tasks. We introduce the Vision-Language Understanding Evaluation benchmark, a multi-task multi-dimension benchmark for evaluating the generalization capabilities and the efficiency-performance trade-off.
• Score: 21.549122658275383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in vision-language pre-training (VLP) have demonstrated impressive performance in a range of vision-language (VL) tasks. However, there exist several challenges for measuring the community's progress in building general multi-modal intelligence. First, most of the downstream VL datasets are annotated using raw images that are already seen during pre-training, which may result in an overestimation of current VLP models' generalization ability. Second, recent VLP work mainly focuses on absolute performance but overlooks the efficiency-performance trade-off, which is also an important indicator for measuring progress. To this end, we introduce the Vision-Language Understanding Evaluation (VLUE) benchmark, a multi-task multi-dimension benchmark for evaluating the generalization capabilities and the efficiency-performance trade-off (``Pareto SOTA'') of VLP models. We demonstrate that there is a sizable generalization gap for all VLP models when testing on out-of-distribution test sets annotated on images from a more diverse distribution that spreads across cultures. Moreover, we find that measuring the efficiency-performance trade-off of VLP models leads to complementary insights for several design choices of VLP. We release the VLUE benchmark to promote research on building vision-language models that generalize well to more diverse images and concepts unseen during pre-training, and are practical in terms of efficiency-performance trade-off.

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