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

With the increasing demand for video understanding, video moment andhighlight detection (MHD) has emerged as a critical research topic. MHD aims tolocalize all moments and predict clip-wise saliency scores simultaneously.Despite progress made by existing DETR-based methods, we observe that thesemethods coarsely fuse features from different modalities, which weakens thetemporal intra-modal context and results in insufficient cross-modalinteraction. To address this issue, we propose MH-DETR (Moment and HighlightDetection Transformer) tailored for MHD. Specifically, we introduce a simpleyet efficient pooling operator within the uni-modal encoder to capture globalintra-modal context. Moreover, to obtain temporally aligned cross-modalfeatures, we design a plug-and-play cross-modal interaction module between theencoder and decoder, seamlessly integrating visual and textual features.Comprehensive experiments on QVHighlights, Charades-STA, Activity-Net, andTVSum datasets show that MH-DETR outperforms existing state-of-the-art methods,demonstrating its effectiveness and superiority. Our code is available athttps://github.com/YoucanBaby/MH-DETR.

MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer