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Register a new userCLIPScore: A Reference-free Evaluation Metric for Image Captioning
Clipscore: مقياس تقييم مجاني للمرجعية
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تم الاعتماد بشكل تقليدي في الصورة على التقييمات التلقائية القائمة على المرجع، حيث يتم مقارنة تعليق الآلات مع التسميات التوضيحية التي كتبها البشر. هذا على النقيض من الطريقة الخالية من المرجعية التي يقوم بها البشر بجودة التسمية التوضيحية. في هذه الورقة، نبلغ عن النتيجة التجريبية المدهشة التي كليب (Radford et al.، 2021)، نموذج متعدد الوسائط مسبقا على أزواج من صورة 400 متر + التسمية التوضيحية من الويب، يمكن استخدامه للتقييم التلقائي القوي لمعالجة الصور دون الحاجة للحصول على المراجع. تثبت التجارب التي تمتد من عدة شرجي أن قيصرنا الجديد المجاني، مقطورات، يحقق أعلى ارتباط بالأحكام البشرية، مما يتفوق على المقاييس القائمة على المرجع الحالي مثل عصير التفاح والتوابل. تثبت تجارب الحصول على المعلومات على أن Clipscore، مع تركيزها الضيق على توافق نص الصورة، هو تكميلية للمقاييس القائمة على مرجعية موجودة تؤكد على أوجه التشابه النصي النصي. وبالتالي، نقدم أيضا نسخة معدنية مرجعية، Refclipscore، والذي يحقق حتى ارتباط أعلى. تتجاوز مهام الوصف الحرفي، تكشف العديد من دراسات الحالة المجالات التي تنفذ فيها clipscore جيدا (صور Clip-Art، تصنيف النص)، ولكن أيضا حيث إنها أضعف نسبيا مقارنة بالمقاييس المستندة إلى المراجع، على سبيل المثال، التسميات التوضيحية الإخبارية التي تتطلب معرفة سياقية أكثر ثراء وبعد
Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in contrast to the reference-free manner in which humans assess caption quality. In this paper, we report the surprising empirical finding that CLIP (Radford et al., 2021), a cross-modal model pretrained on 400M image+caption pairs from the web, can be used for robust automatic evaluation of image captioning without the need for references. Experiments spanning several corpora demonstrate that our new reference-free metric, CLIPScore, achieves the highest correlation with human judgements, outperforming existing reference-based metrics like CIDEr and SPICE. Information gain experiments demonstrate that CLIPScore, with its tight focus on image-text compatibility, is complementary to existing reference-based metrics that emphasize text-text similarities. Thus, we also present a reference-augmented version, RefCLIPScore, which achieves even higher correlation. Beyond literal description tasks, several case studies reveal domains where CLIPScore performs well (clip-art images, alt-text rating), but also where it is relatively weaker in comparison to reference-based metrics, e.g., news captions that require richer contextual knowledge.
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بناء نظام ذكي يقوم بالتعرف على الأصناف الموجودة في صورة وتوليد توصيف نصي لهذه الأغراض الموجودة في الصورة.
استخدمنا الشبكات العصبونية الملتفة Convolutional Neural Networks للقيام بعملية استخلاص الأصناف الموجودة في الصورة، وأدخلنا هذه الأصناف إلى شبكة
عصبونية تكرارية Recurrent Neural Network للقيام بعملية توليد التوصيف النصي.
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