تقرير
Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language
العنوان: | Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language |
---|---|
المؤلفون: | Mercea, Otniel-Bogdan, Riesch, Lukas, Koepke, A. Sophia, Akata, Zeynep |
سنة النشر: | 2022 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing |
الوصف: | Learning to classify video data from classes not included in the training data, i.e. video-based zero-shot learning, is challenging. We conjecture that the natural alignment between the audio and visual modalities in video data provides a rich training signal for learning discriminative multi-modal representations. Focusing on the relatively underexplored task of audio-visual zero-shot learning, we propose to learn multi-modal representations from audio-visual data using cross-modal attention and exploit textual label embeddings for transferring knowledge from seen classes to unseen classes. Taking this one step further, in our generalised audio-visual zero-shot learning setting, we include all the training classes in the test-time search space which act as distractors and increase the difficulty while making the setting more realistic. Due to the lack of a unified benchmark in this domain, we introduce a (generalised) zero-shot learning benchmark on three audio-visual datasets of varying sizes and difficulty, VGGSound, UCF, and ActivityNet, ensuring that the unseen test classes do not appear in the dataset used for supervised training of the backbone deep models. Comparing multiple relevant and recent methods, we demonstrate that our proposed AVCA model achieves state-of-the-art performance on all three datasets. Code and data are available at \url{https://github.com/ExplainableML/AVCA-GZSL}. Comment: CVPR 2022 |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2203.03598 |
رقم الأكسشن: | edsarx.2203.03598 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |