45 citations · 65 across the 12 of their papers we have counts for
13 papers
Transformers from Compressed Representations
Juan C. Leon Alcazar, Mattia Soldan, Mohammad Saatialsoruji +4
Compressed file formats are the corner stone of efficient data storage and transmission, yet their potential for representation learning remains largely underexplored. We introduce…
ResidualViT for Efficient Temporally Dense Video Encoding
Mattia Soldan, Fabian Caba Heilbron, Bernard Ghanem +2
Several video understanding tasks, such as natural language temporal video grounding, temporal activity localization, and audio description generation, require "temporally dense" r…
OpenTAD: A Unified Framework and Comprehensive Study of Temporal Action Detection
Shuming Liu, Chen Zhao, Fatimah Zohra +10
Temporal action detection (TAD) is a fundamental video understanding task that aims to identify human actions and localize their temporal boundaries in videos. Although this field…
Compressed-Language Models for Understanding Compressed File Formats: a JPEG Exploration
Juan C. Pérez, Alejandro Pardo, Mattia Soldan +3
This study investigates whether Compressed-Language Models (CLMs), i.e. language models operating on raw byte streams from Compressed File Formats~(CFFs), can understand files comp…
Towards Automated Movie Trailer Generation
Dawit Mureja Argaw, Mattia Soldan, Alejandro Pardo +4
Movie trailers are an essential tool for promoting films and attracting audiences. However, the process of creating trailers can be time-consuming and expensive. To streamline this…
Boundary-Denoising for Video Activity Localization
Mengmeng Xu, Mattia Soldan, Jialin Gao +3
Video activity localization aims at understanding the semantic content in long untrimmed videos and retrieving actions of interest. The retrieved action with its start and end loca…
Localizing Moments in Long Video Via Multimodal Guidance
Wayner Barrios, Mattia Soldan, Alberto Mario Ceballos-Arroyo +2
The recent introduction of the large-scale, long-form MAD and Ego4D datasets has enabled researchers to investigate the performance of current state-of-the-art methods for video gr…
Egocentric Video-Language Pretraining @ Ego4D Challenge 2022
Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan +13
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Q…
Egocentric Video-Language Pretraining
Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan +13
Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention…
MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions
Mattia Soldan, Alejandro Pardo, Juan León Alcázar +4
The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques. In comparis…
VLG-Net: Video-Language Graph Matching Network for Video Grounding
Mattia Soldan, Mengmeng Xu, Sisi Qu +2
Grounding language queries in videos aims at identifying the time interval (or moment) semantically relevant to a language query. The solution to this challenging task demands unde…
Seq2Seq RNN based Gait Anomaly Detection from Smartphone Acquired Multimodal Motion Data
Riccardo Bonetto, Mattia Soldan, Alberto Lanaro +2
Smartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications. Wearabl…
Finding Moments in Video Collections Using Natural Language
Victor Escorcia, Mattia Soldan, Josef Sivic +2
We introduce the task of retrieving relevant video moments from a large corpus of untrimmed, unsegmented videos given a natural language query. Our task poses unique challenges as…