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Found 10 papers, 5 papers with code
A Language-based solution to enable Metaverse Retrieval
Recently, the Metaverse is becoming increasingly attractive, with millions of users accessing the many available virtual worlds.
FArMARe: a Furniture-Aware Multi-task methodology for Recommending Apartments based on the user interests
Nowadays, many people frequently have to search for new accommodation options.
UniUD Submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023
In this report, we present the technical details of our submission to the EPIC-Kitchens-100 Multi-Instance Retrieval Challenge 2023.
A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval
Data augmentation techniques were introduced to increase the performance on unseen test examples by creating new training samples with the application of semantics-preserving techniques, such as color space or geometric transformations on images.
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-Kitchens-100 Multi-Instance Retrieval Challenge 2022.
Ranked #3 on
Multi-Instance Retrieval
on EPIC-KITCHENS-100
Relevance-based Margin for Contrastively-trained Video Retrieval Models
We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance.
Ranked #7 on
Multi-Instance Retrieval
on EPIC-KITCHENS-100
Learning video retrieval models with relevance-aware online mining
Due to the amount of videos and related captions uploaded every hour, deep learning-based solutions for cross-modal video retrieval are attracting more and more attention.
Ranked #5 on
Multi-Instance Retrieval
on EPIC-KITCHENS-100
SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021
This report presents the technical details of our submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021.
Data augmentation techniques for the Video Question Answering task
Video Question Answering (VideoQA) is a task that requires a model to analyze and understand both the visual content given by the input video and the textual part given by the question, and the interaction between them in order to produce a meaningful answer.
Text-to-Image Synthesis Based on Machine Generated Captions
To address this issue, in this paper we propose an approach capable of generating images starting from a given text using conditional GANs trained on uncaptioned images dataset.
