Robustness in Multimodal Learning under Train-Test Modality Mismatch

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Multimodal learning is defined as learning over multiple heterogeneous input modalities such as video, audio, and text. In this work, we are concerned with understanding how models behave as the type of modalities differ between training and deployment, a situation that naturally arises in many applications of multimodal learning to hardware platforms. We present a …

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Accelerating AI & Innovation: the future of banking depends on core modernization

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In the rapidly evolving landscape of financial services, embracing AI and digital innovation at scale has become imperative for banks to stay competitive. With the power of AI and machine learning, financial institutions can leverage predictive analytics, anomaly detection and shared learning models to enhance system stability, detect fraud and drive superior customer-centric experiences. As …

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AVFormer

AVFormer: Injecting vision into frozen speech models for zero-shot AV-ASR

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Posted by Arsha Nagrani and Paul Hongsuck Seo, Research Scientists, Google Research Automatic speech recognition (ASR) is a well-established technology that is widely adopted for various applications such as conference calls, streamed video transcription and voice commands. While the challenges for this technology are centered around noisy audio inputs, the visual stream in multimodal videos …

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Last Chance! 48-Hour Flash Sale on AI & Chatbot Certified Workshops

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I hope this email finds you well. I wanted to reach out with an exciting update: we are having a 48-Hour Flash Sale on our highly anticipated AI & Chatbot Certified Workshops, and I didn’t want you to miss out on this amazing opportunity! For the next 48 hours only, you can take advantage of …

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Learning Language-Specific Layers for Multilingual Machine Translation

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Multilingual Machine Translation promises to improve translation quality between non-English languages. This is advantageous for several reasons, namely lower latency (no need to translate twice), and reduced error cascades (e.g. , avoiding losing gender and formality information when translating through English). On the downside, adding more languages reduces model capacity per language, which is usually …

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Ensuring the Successful Launch of Ads on Netflix

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By Jose Fernandez, Ed Barker, Hank Jacobs Introduction In November 2022, we introduced a brand new tier — Basic with ads. This tier extended existing infrastructure by adding new backend components and a new remote call to our ads partner on the playback path. As we were gearing up for launch, we wanted to ensure it would go …

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IBM Cloud Databases for Elasticsearch End of Life and pricing changes

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As part of our partnership with Elastic, IBM is announcing the release of a new version of IBM Cloud Databases for Elasticsearch. We are excited to bring you an enhanced offering of our enterprise-ready, fully managed Elasticsearch. Our partnership with Elastic means that we will be able to offer more, richer functionality and world-class levels …

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Retrieval-augmented visual-language pre-training

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Posted by Ziniu Hu, Student Researcher, and Alireza Fathi, Research Scientist, Google Research, Perception Team Large-scale models, such as T5, GPT-3, PaLM, Flamingo and PaLI, have demonstrated the ability to store substantial amounts of knowledge when scaled to tens of billions of parameters and trained on large text and image datasets. These models achieve state-of-the-art …

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byTrack

Implement a multi-object tracking solution on a custom dataset with Amazon SageMaker

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The demand for multi-object tracking (MOT) in video analysis has increased significantly in many industries, such as live sports, manufacturing, and traffic monitoring. For example, in live sports, MOT can track soccer players in real time to analyze physical performance such as real-time speed and moving distance. Since its introduction in 2021, ByteTrack remains to …

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