1 code implementation • 10 Oct 2023 • Cong Yang, Bipin Indurkhya, John See, Bo Gao, Yan Ke, Zeyd Boukhers, Zhenyu Yang, Marcin Grzegorzek
However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards.
no code implementations • 10 May 2023 • Huabin Liu, Weiyao Lin, Tieyuan Chen, Yuxi Li, Shuyuan Li, John See
The alignment model performs temporal and spatial action alignment sequentially at the feature level, leading to more precise measurements of inter-video similarity.
no code implementations • 5 Feb 2023 • Tao Wang, Kean Chen, Weiyao Lin, John See, Zenghui Zhang, Qian Xu, Xia Jia
As such, we propose a novel framework that can effectively predict and mask-out the noisy and confusing detection results before associating the objects into trajectories.
1 code implementation • IEEE Transactions on Image Processing 2023 • JunYi Lim, Vishnu Monn Baskaran, Joanne Mun-Yee Lim, KokSheik Wong, John See, Massimo Tistarelli
Human-Object Interaction (HOI) detection recognizes how persons interact with objects, which is advantageous in autonomous systems such as self-driving vehicles and collaborative robots.
Ranked #7 on Human-Object Interaction Detection on HICO-DET
1 code implementation • 20 Jul 2022 • Huabin Liu, Weixian Lv, John See, Weiyao Lin
In this paper, we propose a novel video frame sampler for few-shot action recognition to address this issue, where task-specific spatial-temporal frame sampling is achieved via a temporal selector (TS) and a spatial amplifier (SA).
no code implementations • 30 Mar 2022 • Rui Qian, Weiyao Lin, John See, Dian Li
The major reason is that the positive pairs, i. e., different clips sampled from the same video, have limited temporal receptive field, and usually share similar background but differ in motions.
no code implementations • CVPR 2022 • Jiahao Fan, Huabin Liu, Wenjie Yang, John See, Aixin Zhang, Weiyao Lin
With the appearance of super high-resolution (e. g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue.
1 code implementation • 2 Nov 2021 • Yuxi Li, Ning Xu, Wenjie Yang, John See, Weiyao Lin
We conduct comprehensive comparison and detailed analysis on challenging benchmarks of DAVIS16, DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is helpful to enhance segmentation quality, improve the robustness of VOS systems, and further provide qualitative comparison and interpretation on how different VOS algorithms work.
1 code implementation • ICCV 2021 • Rui Qian, Yuxi Li, Huabin Liu, John See, Shuangrui Ding, Xian Liu, Dian Li, Weiyao Lin
The crux of self-supervised video representation learning is to build general features from unlabeled videos.
1 code implementation • 10 Jul 2021 • Shuyuan Li, Huabin Liu, Rui Qian, Yuxi Li, John See, Mengjuan Fei, Xiaoyuan Yu, Weiyao Lin
The first stage locates the action by learning a temporal affine transform, which warps each video feature to its action duration while dismissing the action-irrelevant feature (e. g. background).
1 code implementation • 11 Jun 2021 • Gen-Bing Liong, John See, Lai-Kuan Wong
Facial expressions vary from the visible to the subtle.
no code implementations • CVPR 2021 • Yuang Zhang, Huanyu He, Jianguo Li, Yuxi Li, John See, Weiyao Lin
Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods.
1 code implementation • NeurIPS 2020 • Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin
In this paper, we address several inadequacies of current video object segmentation pipelines.
no code implementations • 30 Aug 2020 • Yuxi Li, Weiyao Lin, Tao Wang, John See, Rui Qian, Ning Xu, Li-Min Wang, Shugong Xu
The task of spatial-temporal action detection has attracted increasing attention among researchers.
Ranked #3 on Action Detection on UCF Sports (Video-mAP 0.2 metric)
no code implementations • ECCV 2020 • Yuxi Li, Weiyao Lin, John See, Ning Xu, Shugong Xu, Ke Yan, Cong Yang
Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization.
1 code implementation • 17 Aug 2020 • Kean Chen, Weiyao Lin, Jianguo Li, John See, Ji Wang, Junni Zou
This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem.
1 code implementation • ECCV 2020 • Zhiming Chen, Kean Chen, Weiyao Lin, John See, Hui Yu, Yan Ke, Cong Yang
The experimental results show that PIoU loss can dramatically improve the performance of OBB detectors, particularly on objects with high aspect ratios and complex backgrounds.
2 code implementations • 2019 IEEE International Conference on Image Processing (ICIP) 2019 • Huai-Qian Khor, John See, Sze-Teng Liong, Raphael C. -W. Phan, Weiyao Lin
Micro-expressions are spontaneous, brief and subtle facial muscle movements that exposes underlying emotions.
no code implementations • 17 May 2019 • Weiyao Lin, Yuxi Li, Hao Xiao, John See, Junni Zou, Hongkai Xiong, Jingdong Wang, Tao Mei
The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem. Group re-identification (Re-ID) is a very challenging task sinceit is not only adversely affected by common issues in traditionalsingle object Re-ID problems such as viewpoint and human posevariations, but it also suffers from changes in group layout andgroup membership.
1 code implementation • CVPR 2019 • Kean Chen, Jianguo Li, Weiyao Lin, John See, Ji Wang, Ling-Yu Duan, Zhibo Chen, Changwei He, Junni Zou
For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks.
1 code implementation • 10 Feb 2019 • Sze-Teng Liong, Y. S. Gan, John See, Huai-Qian Khor, Yen-Chang Huang
In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks.
Micro Expression Recognition Micro-Expression Recognition +1
no code implementations • 15 Jun 2018 • Yee-Hui Oh, John See, Anh Cat Le Ngo, Raphael Chung-Wei Phan, Vishnu Monn Baskaran
Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems.
2 code implementations • 22 May 2018 • Huai-Qian Khor, John See, Raphael C. -W. Phan, Weiyao Lin
Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases.
no code implementations • 20 Dec 2017 • Huai-Qian Khor, John See
Video surveillance is a well researched area of study with substantial work done in the aspects of object detection, tracking and behavior analysis.
no code implementations • 9 Jun 2016 • Sze-Teng Liong, John See, Raphael Chung-Wei Phan, Yee-Hui Oh, Anh Cat Le Ngo, KokSheik Wong, Su-Wei Tan
In this paper, we present a novel method for detecting and recognizing micro-expressions by utilizing facial optical strain magnitudes to construct optical strain features and optical strain weighted features.
no code implementations • 6 Jun 2016 • Sze-Teng Liong, John See, KokSheik Wong, Raphael C. -W. Phan
The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression.
Micro Expression Recognition Micro-Expression Recognition +1
no code implementations • 19 Jan 2016 • Anh Cat Le Ngo, John See, Raphael Chung-Wei Phan
Spontaneous subtle emotions are expressed through micro-expressions, which are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great challenge for visual recognition.