1 code implementation • 2 Apr 2024 • Ye Liu, Jixuan He, Wanhua Li, Junsik Kim, Donglai Wei, Hanspeter Pfister, Chang Wen Chen
Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries.
1 code implementation • 31 Mar 2024 • Ye Liu, Jixuan He, Wanhua Li, Junsik Kim, Donglai Wei, Hanspeter Pfister, Chang Wen Chen
Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries.
Ranked #2 on Highlight Detection on QVHighlights
no code implementations • 28 Mar 2024 • Binyuan Huang, Yuqing Wen, Yucheng Zhao, Yaosi Hu, Yingfei Liu, Fan Jia, Weixin Mao, Tiancai Wang, Chi Zhang, Chang Wen Chen, Zhenzhong Chen, Xiangyu Zhang
Autonomous driving progress relies on large-scale annotated datasets.
no code implementations • 25 Mar 2024 • Rui Zhu, Yingwei Pan, Yehao Li, Ting Yao, Zhenglong Sun, Tao Mei, Chang Wen Chen
Despite this progress, mask strategy still suffers from two inherent limitations: (a) training-inference discrepancy and (b) fuzzy relations between mask reconstruction & generative diffusion process, resulting in sub-optimal training of DiT.
no code implementations • 26 Oct 2023 • Runnan Liu, Liang Liu, Yin Xu, Dazhi He, Wenjun Zhang, Chang Wen Chen
We first categorize two types of channel covariance matrix changes based on their impact on system design: Type I change, which denotes the change in the BS receive covariance matrix, and Type II change, which denotes the change in the IRS transmit/receive covariance matrix.
no code implementations • 5 Oct 2023 • Baolin Chong, Hancheng Lu, Langtian Qin, Chenwu Zhang, Jiasen Li, Chang Wen Chen
However, the extensive transmission of video data in IoVT poses challenges in terms of delay and power consumption.
no code implementations • 25 Aug 2023 • Jiaming Shen, Kun Hu, Wei Bao, Chang Wen Chen, Zhiyong Wang
The 2D animation workflow is typically initiated with the creation of keyframes using sketch-based drawing.
no code implementations • 26 Jul 2023 • Fengbo Lan, Chang Wen Chen
The increasing prevalence of mobile devices has led to significant advancements in mobile camera systems and improved image quality.
1 code implementation • ICCV 2023 • Bruce X. B. Yu, Zhi Zhang, Yongxu Liu, Sheng-hua Zhong, Yan Liu, Chang Wen Chen
3D human pose lifting is one of the promising research directions toward the task where both estimated pose and ground truth pose data are used for training.
Ranked #1 on 3D Human Pose Estimation on HumanEva-I
no code implementations • 10 May 2023 • Bruce X. B. Yu, Jianlong Chang, Haixin Wang, Lingbo Liu, Shijie Wang, Zhiyu Wang, Junfan Lin, Lingxi Xie, Haojie Li, Zhouchen Lin, Qi Tian, Chang Wen Chen
With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer.
no code implementations • 22 Mar 2023 • Yan Luo, Ye Liu, Fu-Lai Chung, Yu Liu, Chang Wen Chen
History encoder is designed to model mobility patterns from historical check-in sequences, while query generator explicitly learns user preferences to generate user-specific intention queries.
1 code implementation • CVPR 2023 • Tiancheng Lin, Zhimiao Yu, Hongyu Hu, Yi Xu, Chang Wen Chen
This deficiency is a confounder that limits the performance of existing MIL methods.
no code implementations • 20 Feb 2023 • Yuang Chen, Hancheng Lu, Langtian Qin, Chenwu Zhang, Chang Wen Chen
In this paper, fundamentals and performance tradeoffs of the neXt-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC).
1 code implementation • CVPR 2023 • Junfan Lin, Jianlong Chang, Lingbo Liu, Guanbin Li, Liang Lin, Qi Tian, Chang Wen Chen
During inference, instead of changing the motion generator, our method reformulates the input text into a masked motion as the prompt for the motion generator to ``reconstruct'' the motion.
1 code implementation • 3 Oct 2022 • Bruce X. B. Yu, Jianlong Chang, Lingbo Liu, Qi Tian, Chang Wen Chen
Towards this goal, we propose a framework with a unified view of PETL called visual-PETL (V-PETL) to investigate the effects of different PETL techniques, data scales of downstream domains, positions of trainable parameters, and other aspects affecting the trade-off.
no code implementations • 11 Jul 2022 • Huairui Wang, Zhenzhong Chen, Chang Wen Chen
In this paper, we propose a learned video compression framework via heterogeneous deformable compensation strategy (HDCVC) to tackle the problems of unstable compression performance caused by single-size deformable kernels in downsampled feature domain.
1 code implementation • CVPR 2022 • Ye Liu, Siyuan Li, Yang Wu, Chang Wen Chen, Ying Shan, XiaoHu Qie
Finding relevant moments and highlights in videos according to natural language queries is a natural and highly valuable common need in the current video content explosion era.
Ranked #3 on Highlight Detection on YouTube Highlights
no code implementations • 12 Mar 2022 • Qinyu Li, Tengpeng Li, Hanli Wang, Chang Wen Chen
In this work, a comprehensive study is conducted on video paragraph captioning, with the goal to generate paragraph-level descriptions for a given video.
no code implementations • 10 Mar 2022 • Tengpeng Li, Hanli Wang, Bin He, Chang Wen Chen
Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion.
1 code implementation • 22 Nov 2021 • Ye Liu, Huifang Li, Chao Hu, Shuang Luo, Yan Luo, Chang Wen Chen
The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to aggregate global visual context at feature, spatial, and instance domains, respectively.
no code implementations • 29 Sep 2021 • Tao Wei, Yonghong Tian, YaoWei Wang, Yun Liang, Chang Wen Chen
In this research, we propose a novel and principled operator called optimized separable convolution by optimal design for the internal number of groups and kernel sizes for general separable convolutions can achieve the complexity of O(C^{\frac{3}{2}}K).
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 Jan 2021 • Tao Wei, Angelica I Aviles-Rivero, Shuo Wang, Yuan Huang, Fiona J Gilbert, Carola-Bibiane Schönlieb, Chang Wen Chen
The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning.
Cancer-no cancer per image classification Image Classification +3
no code implementations • 1 Jan 2021 • Tao Wei, Yonghong Tian, Chang Wen Chen
In this research, we propose a novel operator called \emph{optimal separable convolution} which can be calculated at $O(C^{\frac{3}{2}}KHW)$ by optimal design for the internal number of groups and kernel sizes for general separable convolutions.
2 code implementations • 14 Aug 2020 • Ye Liu, Junsong Yuan, Chang Wen Chen
We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of <human, action, object> in images.
no code implementations • 13 Jul 2020 • Lifang Wu, Zhou Yang, Qi. Wang, Meng Jian, Boxuan Zhao, Junchi Yan, Chang Wen Chen
Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos.
no code implementations • 6 Jun 2019 • Yu Liu, Li Deng, Jianshu Chen, Chang Wen Chen
To remove the need for the parallel training corpora has practical significance for real-world applications, and it is one of the main goals of unsupervised learning.
1 code implementation • ICCV 2019 • Guo-Jun Qi, Liheng Zhang, Chang Wen Chen, Qi Tian
This ensures the resultant TERs of individual images contain the {\em intrinsic} information about their visual structures that would equivary {\em extricably} under various transformations in a generalized {\em nonlinear} case.
no code implementations • 16 Mar 2019 • Lifang Wu, Zhou Yang, Jiaoyu He, Meng Jian, Yaowen Xu, Dezhong Xu, Chang Wen Chen
Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion.
no code implementations • CVPR 2018 • Shuang Ma, Jianlong Fu, Chang Wen Chen, Tao Mei
Specifically, we jointly learn a deep attention encoder, and the instance-level correspondences could be consequently discovered through attending on the learned instances.
no code implementations • CVPR 2018 • Shuang Ma, Jianlong Fu, Chang Wen Chen, Tao Mei
Specifically, we jointly learn a deep attention encoder, and the instancelevel correspondences could be consequently discovered through attending on the learned instance pairs.
no code implementations • 19 Jan 2018 • Jing Zhang, Yang Cao, Yang Wang, Chenglin Wen, Chang Wen Chen
Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties.
no code implementations • ICLR 2018 • Tao Wei, Changhu Wang, Chang Wen Chen
In this research, we present a novel learning scheme called network iterative learning for deep neural networks.
no code implementations • CVPR 2017 • Jing Zhang, Yang Cao, Shuai Fang, Yu Kang, Chang Wen Chen
Then, we propose a simple but effective image prior, maximum reflectance prior, to estimate the varying ambient illumination.
no code implementations • CVPR 2017 • Shuang Ma, Jing Liu, Chang Wen Chen
However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input.
Ranked #2 on Aesthetics Quality Assessment on AVA
no code implementations • 12 Jan 2017 • Tao Wei, Changhu Wang, Chang Wen Chen
Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i. e., how a convolutional layer can be morphed into an arbitrary module of a neural network.
no code implementations • 2 Jun 2016 • Yu Liu, Jianlong Fu, Tao Mei, Chang Wen Chen
Second, by using sGRU as basic units, the BMRNN is trained to align the local storylines into the global sequential timeline.
no code implementations • 5 Mar 2016 • Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen
The second requirement for this network morphism is its ability to deal with non-linearity in a network.