Search Results for author: Mengmeng Xu

Found 13 papers, 5 papers with code

ETAD: A Unified Framework for Efficient Temporal Action Detection

1 code implementation14 May 2022 Shuming Liu, Mengmeng Xu, Chen Zhao, Xu Zhao, Bernard Ghanem

Interestingly, on ActivityNet-1. 3, it reaches 37. 78% average mAP, while only requiring 6 mins of training time and 1. 23 GB memory based on pre-extracted features.

Action Detection Video Understanding

SegTAD: Precise Temporal Action Detection via Semantic Segmentation

no code implementations3 Mar 2022 Chen Zhao, Merey Ramazanova, Mengmeng Xu, Bernard Ghanem

To address these issues and precisely model temporal action detection, we formulate the task of temporal action detection in a novel perspective of semantic segmentation.

Action Detection Object Detection +1

Ego4D: Around the World in 3,000 Hours of Egocentric Video

no code implementations13 Oct 2021 Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei HUANG, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.

De-identification

Relation-aware Video Reading Comprehension for Temporal Language Grounding

1 code implementation EMNLP 2021 Jialin Gao, Xin Sun, Mengmeng Xu, Xi Zhou, Bernard Ghanem

Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence.

Reading Comprehension

VLG-Net: Video-Language Graph Matching Network for Video Grounding

1 code implementation19 Nov 2020 Mattia Soldan, Mengmeng Xu, Sisi Qu, Jesper Tegner, Bernard Ghanem

Grounding language queries in videos aims at identifying the time interval (or moment) semantically relevant to a language query.

Graph Matching Moment Retrieval +2

LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks

no code implementations24 Aug 2020 Guohao Li, Mengmeng Xu, Silvio Giancola, Ali Thabet, Bernard Ghanem

In this paper, we introduce a new NAS framework, dubbed LC-NAS, where we search for point cloud architectures that are constrained to a target latency.

Neural Architecture Search Point Cloud Classification +2

Learning Heat Diffusion for Network Alignment

no code implementations10 Jul 2020 Sisi Qu, Mengmeng Xu, Bernard Ghanem, Jesper Tegner

EDNA uses the diffusion signal as a proxy for computing node similarities between networks.

G-TAD: Sub-Graph Localization for Temporal Action Detection

4 code implementations CVPR 2020 Mengmeng Xu, Chen Zhao, David S. Rojas, Ali Thabet, Bernard Ghanem

In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem.

Temporal Action Localization

BAOD: Budget-Aware Object Detection

no code implementations10 Apr 2019 Alejandro Pardo, Mengmeng Xu, Ali Thabet, Pablo Arbelaez, Bernard Ghanem

We adopt a hybrid supervised learning framework to train the object detector from both these types of annotation.

Active Learning Object Detection

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