no code implementations • 18 Mar 2024 • Siying Liu, Pier Luigi Dragotti
To address this, we propose warping the input intensity frames and sparse codes to enhance reconstruction quality.
1 code implementation • 24 Jan 2024 • Shuokang Huang, Kaihan Li, Di You, Yichong Chen, Arvin Lin, Siying Liu, Xiaohui Li, Julie A. McCann
WiFi-based human sensing has exhibited remarkable potential to analyze user behaviors in a non-intrusive and device-free manner, benefiting applications as diverse as smart homes and healthcare.
1 code implementation • Frontiers in Neuroscience 2023 • Siying Liu, Vincent C. H. Leung, Pier Luigi Dragotti
In the backpropagation, we develop an error assignment method that propagates error from FS times to spikes through a Gaussian window, and then supervised learning for spikes is implemented through a surrogate gradient approach.
1 code implementation • IEEE Transactions on Pattern Analysis and Machine Intelligence 2023 • Siying Liu, Pier Luigi Dragotti
In this paper, we propose a light, simple model-based deep network for E2V reconstruction, explore the diversity for adjacent pixels in V2E generation, and finally build a video-to-events-to-video (V2E2V) architecture to validate how alternative event generation strategies improve video reconstruction.
1 code implementation • journal 2023 • Lianwei Wu, Pusheng Liu, Yuheng Yuan, Siying Liu, Yanning Zhang
Neural text transfer aims to change the style of a text sequence while keeping its original content.
1 code implementation • ECCV 2022 • Wen-Yan Lin, Zhonghang Liu, Siying Liu
Unsupervised anomaly detection on image data is notoriously unstable.
Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly +3
1 code implementation • 4 Oct 2022 • Wen-Yan Lin, Siying Liu, Bing Tian Dai, Hongdong Li
We use the model to develop a classification scheme which suppresses the impact of noise while preserving semantic cues.
1 code implementation • IEEE Transactions on Pattern Analysis and Machine Intelligence 2021 • Wen-Yan Lin, Siying Liu, Changhao Ren, Ngai-Man Cheung, Hongdong Li, Yasuyuki Matsushita
The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically.
Ranked #1 on Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly on STL-10 (using extra training data)
no code implementations • Winter Conference on Applications of Computer Vision 2021 • Tangqing Li, Zheng Wang, Siying Liu, Wen-Yan Lin
This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting.
no code implementations • 23 Sep 2020 • Huajian Huang, Wen-Yan Lin, Siying Liu, Dong Zhang, Sai-Kit Yeung
As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle.
1 code implementation • CVPR 2018 • Wen-Yan Lin, Siying Liu, Jian-Huang Lai, Yasuyuki Matsushita
Many high dimensional vector distances tend to a constant.
no code implementations • ICCV 2015 • Siying Liu, Tian-Tsong Ng, Kalyan Sunkavalli, Minh N. Do, Eli Shechtman, Nathan Carr
In this work, we investigate the problem of automatically inferring the lattice structure of near-regular textures (NRT) in real-world images.