Exploiting Semantic Role Contextualized Video Features for Multi-Instance Text-Video Retrieval EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022

06/29/2022
by   Burak Satar, et al.
0

In this report, we present our approach for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022. We first parse sentences into semantic roles corresponding to verbs and nouns; then utilize self-attentions to exploit semantic role contextualized video features along with textual features via triplet losses in multiple embedding spaces. Our method overpasses the strong baseline in normalized Discounted Cumulative Gain (nDCG), which is more valuable for semantic similarity. Our submission is ranked 3rd for nDCG and ranked 4th for mAP.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/18/2021

On Semantic Similarity in Video Retrieval

Current video retrieval efforts all found their evaluation on an instanc...
research
06/27/2023

UniUD Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023

In this report, we present the technical details of our submission to th...
research
06/22/2022

UniUD-FBK-UB-UniBZ Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2022

This report presents the technical details of our submission to the EPIC...
research
06/26/2022

Semantic Role Aware Correlation Transformer for Text to Video Retrieval

With the emergence of social media, voluminous video clips are uploaded ...
research
09/05/2017

Predicting Visual Features from Text for Image and Video Caption Retrieval

This paper strives to find amidst a set of sentences the one best descri...
research
06/26/2022

RoME: Role-aware Mixture-of-Expert Transformer for Text-to-Video Retrieval

Seas of videos are uploaded daily with the popularity of social channels...
research
05/16/2023

Hybrid and Collaborative Passage Reranking

In passage retrieval system, the initial passage retrieval results may b...

Please sign up or login with your details

Forgot password? Click here to reset