Search Results for author:
Found 9 papers, 8 papers with code
Towards Automated Movie Trailer Generation
Movie trailers are an essential tool for promoting films and attracting audiences.
Boundary-Denoising for Video Activity Localization
To alleviate the boundary ambiguity, we propose to study the video activity localization problem from a denoising perspective.
Ranked #1 on
Video Grounding
on MAD
Localizing Moments in Long Video Via Multimodal Guidance
In this paper, we propose a method for improving the performance of natural language grounding in long videos by identifying and pruning out non-describable windows.
Ranked #1 on
Natural Language Moment Retrieval
on MAD
Natural Language Moment Retrieval
Natural Language Visual Grounding
+2
Egocentric Video-Language Pretraining @ Ego4D Challenge 2022
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 Query (MQ), Object State Change Classification (OSCC), and PNR Localization (PNR).
Egocentric Video-Language Pretraining
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
The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques.
Ranked #2 on
Natural Language Moment Retrieval
on MAD
VLG-Net: Video-Language Graph Matching Network for Video Grounding
Grounding language queries in videos aims at identifying the time interval (or moment) semantically relevant to a language query.
Ranked #1 on
Natural Language Moment Retrieval
on DiDeMo
Seq2Seq RNN based Gait Anomaly Detection from Smartphone Acquired Multimodal Motion Data
Smartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications.
Finding Moments in Video Collections Using Natural Language
We evaluate our approach on two recently proposed datasets for temporal localization of moments in video with natural language (DiDeMo and Charades-STA) extended to our video corpus moment retrieval setting.
