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Register a new userZero-Shot Compositional Concept Learning
التعلم مفهوم التركيب صفر
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في هذه الورقة، ندرس مشكلة الاعتراف بمفاهيم كائن السمات التركيبية داخل إطار التعلم الصفرية (ZSL). نقترح شبكة اعتقالة على الحلقة (EPICA) التي تعتمد على الحلقة التي تجمع بين مزايا آلية الانتباه العابر واستراتيجية التدريب القائمة على الحلقة للتعرف على المفاهيم التركيبية الجديدة. أولا، قواعد ابيكا على الانتباه إلى ربط المعلومات المفاهيمية وتستخدم طبقة تجمع بوابات لبناء تمثيلات سياقية لكل من الصور والمفاهيم. يتم استخدام التمثيلات المحدثة لحساب ذات الصلة متعددة الوسائط أكثر فائدة للتعرف على المفهوم. ثانيا، يتم اعتماد استراتيجية تدريب الحلقة ذات الطورين، وخاصة المرحلة الرانسطة، للاستفادة من أمثلة الاختبار غير المسبقة لتخفيف مشكلة تعلم الموارد المنخفضة الموارد. أظهرت التجارب على معايير التعلم التركيبية المتفوقة (ZSCL) المتسعة (ZSCL) فعالية النموذج مقارنة بالنهج الحديثة على كل من إعدادات ZSCL التقليدية والعالمية.
In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework. We propose an episode-based cross-attention (EpiCA) network which combines merits of cross-attention mechanism and episode-based training strategy to recognize novel compositional concepts. Firstly, EpiCA bases on cross-attention to correlate conceptvisual information and utilizes the gated pooling layer to build contextualized representations for both images and concepts. The updated representations are used for a more indepth multi-modal relevance calculation for concept recognition. Secondly, a two-phase episode training strategy, especially the ransductive phase, is adopted to utilize unlabeled test examples to alleviate the low-resource learning problem. Experiments on two widelyused zero-shot compositional learning (ZSCL) benchmarks have demonstrated the effectiveness of the model compared with recent approaches on both conventional and generalized ZSCL settings.
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