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Eric Zhongcong Xu

dblp:304/2934 · DBLP profile ↗
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5ranked-venue papers
2as first author
5since 2021 · last 2025
0000-0003-3511-8466ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 4 · 1 first-author · 4 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
4 papers
Video understanding and tracking · 58% Vision and language · 18% Generative modeling · 10%
Computer graphics and multimedia
3 papers
Audio and music processing · 42% Multimedia analysis and retrieval · 27% Visual content generation and editing · 24%

Topics — the 15 heaviest of 15, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking
egocentric video understanding		 1.632025
Ego4D: Around the World in 3,600 Hours of Egocentric Video · IEEE Trans. Pattern Anal. Mach. Intell. 2025
Ego4D: Around the World in 3, 000 Hours of Egocentric Video · CVPR 2022
Egocentric Video-Language Pretraining · NeurIPS 2022
Computer vision › Video understanding and tracking
activity prediction		 0.912025
Ego4D: Around the World in 3,600 Hours of Egocentric Video · IEEE Trans. Pattern Anal. Mach. Intell. 2025
Machine learning › Generative modeling
3d generative model		 0.712023
PV3D: A 3D Generative Model for Portrait Video Generation · ICLR 2023
Visual content generation and editing › video generation
portrait video generation		 0.712023
PV3D: A 3D Generative Model for Portrait Video Generation · ICLR 2023
Computer vision › Video understanding and tracking
activity understanding		 0.612022
Ego4D: Around the World in 3, 000 Hours of Egocentric Video · CVPR 2022
Machine learning › Representation and self-supervised learning
contrastive learning		 0.612022
Egocentric Video-Language Pretraining · NeurIPS 2022
Computer vision › Vision and language › vision-language pretraining
egocentric video-language pretraining		 0.612022
Egocentric Video-Language Pretraining · NeurIPS 2022
Computer vision › Video understanding and tracking › egocentric video understanding
first-person activity recognition		 0.612022
Ego4D: Around the World in 3, 000 Hours of Egocentric Video · CVPR 2022
Computer vision › Vision and language › vision-language pretraining
video-language pre-training		 0.612022
Egocentric Video-Language Pretraining · NeurIPS 2022
Audio and music processing › speaker diarization
audio-visual speaker diarization		 0.612022
AVA-AVD: Audio-visual Speaker Diarization in the Wild · ACM Multimedia 2022
Multimedia analysis and retrieval
multimodal benchmark		 0.612022
AVA-AVD: Audio-visual Speaker Diarization in the Wild · ACM Multimedia 2022
Audio and music processing
speaker diarization		 0.612022
AVA-AVD: Audio-visual Speaker Diarization in the Wild · ACM Multimedia 2022
Computer vision › 3D vision
3d scene reconstruction		 0.312025
Ego4D: Around the World in 3,600 Hours of Egocentric Video · IEEE Trans. Pattern Anal. Mach. Intell. 2025
Geometric modeling and processing
3d reconstruction		 0.212023
PV3D: A 3D Generative Model for Portrait Video Generation · ICLR 2023
Multimedia analysis and retrieval › video dataset
video dataset benchmark		 0.212022
Ego4D: Around the World in 3, 000 Hours of Egocentric Video · CVPR 2022

Methods — techniques the papers use, named apart from their topics

neural rendering · 1.33d generative modeling · 1.3negative sample mining · 0.6modality mask · 0.6contrastive learning · 0.6audio-visual relation network · 0.6
YearPublicationVenuePosition
2025 Ego4D: Around the World in 3,600 Hours of Egocentric Video
abstract
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception.
Kristen Grauman, Andrew Westbury, Eugene Byrne, Vincent Cartillier, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang 0007, Devansh Kukreja, Miao Liu 0007, Xingyu Liu 0001, Tushar Nagarajan, Ilija Radosavovic, Santhosh K. Ramakrishnan, Fiona Ryan, Jayant Sharma 0002, Michael Wray, Mengmeng Xu 0006, Eric Zhongcong Xu, Chen Zhao 0002, Siddhant Bansal, Dhruv Batra, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina González, James Hillis, Xuhua Huang, Yifei Huang 0002, Wenqi Jia 0001, Weslie Khoo, Jáchym Kolár, Satwik Kottur, Anurag Kumar 0003, Federico Landini, Yanghao Li, Zhenqiang Li 0002, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran K. Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Xindi Wu, Takuma Yagi, Ziwei Zhao 0003, Yunyi Zhu, Pablo Andrés Arbeláez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fügen, Bernard Ghanem, Vamsi K. Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Makoto Kitani, Haizhou Li 0001, Richard A. Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Jianbo Shi, Zheng Shou 0001, Antonio Torralba 0001, Lorenzo Torresani, Mingfei Yan, Jitendra Malik
 IEEE Trans. Pattern Anal. Mach. Intell.21
2023 PV3D: A 3D Generative Model for Portrait Video Generation
Eric Zhongcong Xu, Jun Hao Liew, Song Bai 0001, Jiashi Feng, Zheng Shou 0001
 ICLR1
2022 Ego4D: Around the World in 3, 000 Hours of Egocentric Video
abstract
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/
Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang 0007, Miao Liu 0007, Xingyu Liu 0001, Tushar Nagarajan, Ilija Radosavovic, Santhosh K. Ramakrishnan, Fiona Ryan, Jayant Sharma 0002, Michael Wray, Mengmeng Xu 0006, Eric Zhongcong Xu, Chen Zhao 0002, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina González, James Hillis, Xuhua Huang, Yifei Huang 0002, Wenqi Jia 0001, Weslie Khoo, Jáchym Kolár, Satwik Kottur, Anurag Kumar 0003, Federico Landini, Yanghao Li, Zhenqiang Li 0002, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran K. Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Xindi Wu, Takuma Yagi, Ziwei Zhao 0003, Yunyi Zhu, Pablo Andrés Arbeláez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fügen, Bernard Ghanem, Vamsi K. Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Makoto Kitani, Haizhou Li 0001, Richard A. Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Jianbo Shi, Zheng Shou 0001, Antonio Torralba 0001, Lorenzo Torresani, Mingfei Yan, Jitendra Malik
 CVPR19
2022 AVA-AVD: Audio-visual Speaker Diarization in the Wild
abstract
Audio-visual speaker diarization aims at detecting "who spoke when'' using both auditory and visual signals. Existing audio-visual diarization datasets are mainly focused on indoor environments like meeting rooms or news studios, which are quite different from in-the-wild videos in many scenarios such as movies, documentaries, and audience sitcoms. To develop diarization methods for these challenging videos, we create the AVA Audio-Visual Diarization (AVA-AVD) dataset. Our experiments demonstrate that adding AVA-AVD into training set can produce significantly better diarization models for in-the-wild videos despite that the data is relatively small. Moreover, this benchmark is challenging due to the diverse scenes, complicated acoustic conditions, and completely off-screen speakers. As a first step towards addressing the challenges, we design the Audio-Visual Relation Network (AVR-Net) which introduces a simple yet effective modality mask to capture discriminative information based on face visibility. Experiments show that our method not only can outperform state-of-the-art methods but is more robust as varying the ratio of off-screen speakers. Our data and code has been made publicly available at \textcolormagenta \urlhttps://github.com/showlab/AVA-AVD .
Eric Zhongcong Xu, Zeyang Song, Satoshi Tsutsui, Mang Ye, Zheng Shou 0001
 ACM Multimedia1
2022 Egocentric Video-Language Pretraining
abstract
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. Best performing works rely on large-scale, 3rd-person video-text datasets, such as HowTo100M. In this work, we exploit the recently released Ego4D dataset to pioneer Egocentric VLP along three directions. (i) We create EgoClip, a 1st-person video-text pretraining dataset comprising 3.8M clip-text pairs well-chosen from Ego4D, covering a large variety of human daily activities. (ii) We propose a novel pretraining objective, dubbed EgoNCE, which adapts video-text contrastive learning to the egocentric domain by mining egocentric-aware positive and negative samples. (iii) We introduce EgoMCQ, a development benchmark that is close to EgoClip and hence can support effective validation and fast exploration of our design decisions in EgoClip and EgoNCE. Furthermore, we demonstrate strong performance on five egocentric downstream tasks across three datasets: video-text retrieval on EPIC-KITCHENS-100; action recognition on Charades-Ego; natural language query, moment query, and object state change classification on Ego4D challenge benchmarks. The dataset and code are available at https://github.com/showlab/EgoVLP.
Qinghong Lin, Jinpeng Wang 0001, Mattia Soldan, Michael Wray, Rui Yan 0001, Eric Zhongcong Xu, Difei Gao, Rongcheng Tu, Weijie Kong, Chengfei Cai, Hongfa Wang, Dima Damen, Bernard Ghanem, Wei Liu 0005, Zheng Shou 0001
 NeurIPS6