no code implementations • 25 Apr 2023 • Jiacheng Wang, Ping Liu, Jingen Liu, Wei Xu
To address these limitations, we propose a Text-guided Eyeglasses Manipulation method that allows for control of the eyeglasses shape and style based on a binary mask and text, respectively.
no code implementations • 27 Jun 2022 • Jiyang Yu, Jingen Liu, Jing Huang, Wei zhang, Tao Mei
To this end, we propose a novel network to encode face videos into the latent space of StyleGAN for semantic face video manipulation.
no code implementations • 2 Apr 2022 • Akash Gupta, Jingen Liu, Liefeng Bo, Amit K. Roy-Chowdhury, Tao Mei
To incorporate this ability in intelligent systems a question worth pondering upon is how exactly do we anticipate?
no code implementations • 18 Jan 2022 • Zhengyuan Yang, Jingen Liu, Jing Huang, Xiaodong He, Tao Mei, Chenliang Xu, Jiebo Luo
In this study, we aim to predict the plausible future action steps given an observation of the past and study the task of instructional activity anticipation.
no code implementations • 11 Jan 2022 • Yingwei Pan, Yue Chen, Qian Bao, Ning Zhang, Ting Yao, Jingen Liu, Tao Mei
To our best knowledge, our system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events.
no code implementations • 28 Nov 2021 • Yang Peng, Ping Liu, Yawei Luo, Pan Zhou, Zichuan Xu, Jingen Liu
Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value.
Domain Adaptive Person Re-Identification Person Re-Identification
no code implementations • 10 Nov 2021 • Yi Lin, Jianchao Su, Xiang Wang, Xiang Li, Jingen Liu, Kwang-Ting Cheng, Xin Yang
We have evaluated our approach using the 20 CTPA test dataset from the PE challenge, achieving a sensitivity of 78. 9%, 80. 7% and 80. 7% at 2 false positives per volume at 0mm, 2mm and 5mm localization error, which is superior to the state-of-the-art methods.
no code implementations • 4 Oct 2021 • Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, JiQuan Pei, JinFeng Yi, BoWen Zhou
In this review, we provide AI practitioners with a comprehensive guide for building trustworthy AI systems.
1 code implementation • 30 Sep 2021 • Xiao Wang, Jingen Liu, Tao Mei, Jiebo Luo
Unlike the mainstream clustering-based methods, our framework exploits a transformer-based feature reconstruction scheme to detect event boundary by reconstruction errors.
1 code implementation • CVPR 2022 • Jiyang Yu, Jingen Liu, Liefeng Bo, Tao Mei
Those methods achieve limited performance as they suffer from the challenge in spatial frame alignment and the lack of useful information from similar LR neighbor frames.
Ranked #7 on Analog Video Restoration on TAPE
1 code implementation • ICCV 2021 • Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, Chang Wen Chen
To our knowledge, this is the first attempt of its kind.
no code implementations • 20 Jun 2021 • Ping Liu, Yuewei Lin, Yang He, Yunchao Wei, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh, Jingen Liu
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.
no code implementations • CVPR 2021 • Kedan Li, Min Jin Chong, Jeffrey Zhang, Jingen Liu
Prior works produce images that are filled with artifacts and fail to capture important visual details necessary for commercial applications.
no code implementations • CVPR 2021 • Wang Luo, Tianzhu Zhang, Wenfei Yang, Jingen Liu, Tao Mei, Feng Wu, Yongdong Zhang
In this paper, we present an Action Unit Memory Network (AUMN) for weakly supervised temporal action localization, which can mitigate the above two challenges by learning an action unit memory bank.
Ranked #7 on Weakly Supervised Action Localization on THUMOS14
Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1
1 code implementation • CVPR 2020 • Qi Cai, Yingwei Pan, Yu Wang, Jingen Liu, Ting Yao, Tao Mei
To this end, we devise a general loss function to cover most region-based object detectors with various sampling strategies, and then based on it we propose a unified sample weighting network to predict a sample's task weights.
no code implementations • 13 May 2020 • Ning Zhang, Jingen Liu, Ke Wang, Dan Zeng, Tao Mei
Inspired by the human "visual tracking" capability which leverages motion cues to distinguish the target from the background, we propose a Two-Stream Residual Convolutional Network (TS-RCN) for visual tracking, which successfully exploits both appearance and motion features for model update.
no code implementations • 22 Mar 2020 • Kedan Li, Min Jin Chong, Jingen Liu, David Forsyth
However, obtaining a realistic image is challenging because the kinematics of garments is complex and because outline, texture, and shading cues in the image reveal errors to human viewers.
no code implementations • 12 Dec 2019 • Jui-Hsin Lai, Bo Wu, Xin Wang, Dan Zeng, Tao Mei, Jingen Liu
This model associates themes with the pairwise compatibility with attention, and thus compute the outfit-wise compatibility.
no code implementations • CVPR 2019 • Yiheng Zhang, Zhaofan Qiu, Jingen Liu, Ting Yao, Dong Liu, Tao Mei
As a result, our CAS is able to search an optimized architecture with customized constraints.
no code implementations • 28 Nov 2018 • Longlong Jing, Xiaodong Yang, Jingen Liu, YingLi Tian
The success of deep neural networks generally requires a vast amount of training data to be labeled, which is expensive and unfeasible in scale, especially for video collections.
Ranked #42 on Self-Supervised Action Recognition on HMDB51
Self-Supervised Action Recognition Temporal Action Localization +1
no code implementations • 2 Dec 2015 • Mohamed Elhoseiny, Jingen Liu, Hui Cheng, Harpreet Sawhney, Ahmed Elgammal
To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) automatically determining relevance of concepts/attributes to a free text query, which could be useful for other applications, and (c) retrieving videos by free text event query (e. g., "changing a vehicle tire") based on their content.
no code implementations • CVPR 2014 • Jiejie Zhu, Omar Javed, Jingen Liu, Qian Yu, Hui Cheng, Harpreet Sawhney
In this paper, we give a comparative evaluation of the proposed method and demonstrate that MIMS outperforms the state of the art approaches in identifying pedestrians from low resolution airborne videos.