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

Consider end-to-end training of a multi-modal vs. a single-modal network on atask with multiple input modalities: the multi-modal network receives moreinformation, so it should match or outperform its single-modal counterpart. Inour experiments, however, we observe the opposite: the best single-modalnetwork always outperforms the multi-modal network. This observation isconsistent across different combinations of modalities and on different tasksand benchmarks.  This paper identifies two main causes for this performance drop: first,multi-modal networks are often prone to overfitting due to increased capacity.Second, different modalities overfit and generalize at different rates, sotraining them jointly with a single optimization strategy is sub-optimal. Weaddress these two problems with a technique we call Gradient Blending, whichcomputes an optimal blend of modalities based on their overfitting behavior. Wedemonstrate that Gradient Blending outperforms widely-used baselines foravoiding overfitting and achieves state-of-the-art accuracy on various tasksincluding human action recognition, ego-centric action recognition, andacoustic event detection.

What Makes Training Multi-Modal Classification Networks Hard?