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سؤال الفيديو يجيب مع عبارات الأدوار الدلالية
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تقتصر مقاييس تقييم سؤال الفيديو (VIDQA) على إجابة كلمة واحدة أو اختيار عبارة من مجموعة ثابتة من العبارات.هذه المقاييس تحد من سيناريو تطبيق نماذج VIDQA.في هذا العمل، نستفيد الأدوار الدلالية المستمدة من أوصاف الفيديو لإخفاء عبارات معينة، لإدخال VIDQAP الذي يطرح VIDQA كامرأة تعبئة العبارة.لتمكين تقييم الجمل الإجابة، نحسب التحسين النسبي للإجابة المتوقعة مقارنة بسلسلة فارغة.لتقليل تأثير التحيز اللغوي في مجموعات بيانات VIDQA، نسترجع شريط فيديو له إجابة مختلفة لنفس السؤال.لتسهيل البحث، نقوم ببناء AttactNet-SRL-QA و Charads-SRL-QA ومقاييسهم عن طريق تمديد ثلاث نماذج لغة رؤية.نحن نقوم بإجراء تحليل مكثف ودراسات ablative لتوجيه العمل في المستقبل.الرمز والبيانات عامة.
Video Question Answering (VidQA) evaluation metrics have been limited to a single-word answer or selecting a phrase from a fixed set of phrases. These metrics limit the VidQA models' application scenario. In this work, we leverage semantic roles derived from video descriptions to mask out certain phrases, to introduce VidQAP which poses VidQA as a fill-in-the-phrase task. To enable evaluation of answer phrases, we compute the relative improvement of the predicted answer compared to an empty string. To reduce the influence of language bias in VidQA datasets, we retrieve a video having a different answer for the same question. To facilitate research, we construct ActivityNet-SRL-QA and Charades-SRL-QA and benchmark them by extending three vision-language models. We perform extensive analysis and ablative studies to guide future work. Code and data are public.
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