Multi-heads of both self and cross attentions

The composed video retrieval (CoVR) task is a new task, where the goal is to find a video that matches both a query image and a query text. The query image represents a visual concept that the user is interested in, and the query text specifies how the concept should be modified or refined. For example, given an image of a fountain and the text _during show at night_, the CoVR task is to retrieve a video that shows the fountain at night with a show.

CoVR

CoVR: Learning Composed Video Retrieval from Web Video Captions

Rendezvous

Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos

**ProphetNet** is a sequence-to-sequence pre-training model that introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of optimizing one-step-ahead prediction in the traditional sequence-to-sequence model, the ProphetNet is optimized by $n$-step ahead prediction that predicts the next $n$ tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and further help predict multiple future tokens.

Source	Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com