English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper tackles the problem of processing and combining efficientlyarbitrary long data streams, coming from different modalities with differentacquisition frequencies. Common applications can be, for instance, long-timeindustrial or real-life systems monitoring from multimodal heterogeneous data(sensor data, monitoring report, images, etc.). To tackle this problem, wepropose StreaMulT, a Streaming Multimodal Transformer, relying on cross-modalattention and an augmented memory bank to process arbitrary long inputsequences at training time and run in a streaming way at inference. StreaMulTreproduces state-of-the-art results on CMU-MOSEI dataset, while being able todeal with much longer inputs than other models such as previous MultimodalTransformer.

StreaMulT: Streaming Multimodal Transformer for Heterogeneous and Arbitrary Long Sequential Data