1 code implementation • 31 Mar 2024 • Wenxiao Deng, Wenbin Li, Tianyu Ding, Lei Wang, Hongguang Zhang, Kuihua Huang, Jing Huo, Yang Gao
However, these methods face two primary limitations: the dispersed feature distribution within the same class in synthetic datasets, reducing class discrimination, and an exclusive focus on mean feature consistency, lacking precision and comprehensiveness.
1 code implementation • 14 Feb 2023 • Hongguang Zhang, Limeng Zhang, Yuchao Dai, Hongdong Li, Piotr Koniusz
Contemporary deep learning multi-scale deblurring models suffer from many issues: 1) They perform poorly on non-uniformly blurred images/videos; 2) Simply increasing the model depth with finer-scale levels cannot improve deblurring; 3) Individual RGB frames contain a limited motion information for deblurring; 4) Previous models have a limited robustness to spatial transformations and noise.
1 code implementation • 15 Jan 2022 • Hongguang Zhang, Hongdong Li, Piotr Koniusz
The goal of multi-level feature design is to extract feature representations at different layer-wise levels of CNN, realizing several levels of visual abstraction to achieve robust few-shot learning.
no code implementations • 27 Dec 2020 • Piotr Koniusz, Hongguang Zhang
Our layer combines the feature vectors and their respective spatial locations in the feature maps produced by the last convolutional layer of CNN into a positive definite matrix with second-order statistics to which PN operators are applied, forming so-called Second-order Pooling (SOP).
1 code implementation • CVPR 2021 • Liyuan Pan, Shah Chowdhury, Richard Hartley, Miaomiao Liu, Hongguang Zhang, Hongdong Li
The heavy defocus blur in DP pairs affects the performance of matching-based depth estimation approaches.
1 code implementation • ECCV 2020 • Hongguang Zhang, Li Zhang, Xiaojuan Qi, Hongdong Li, Philip H. S. Torr, Piotr Koniusz
Such encoded blocks are aggregated by permutation-invariant pooling to make our approach robust to varying action lengths and long-range temporal dependencies whose patterns are unlikely to repeat even in clips of the same class.
Ranked #6 on Few Shot Action Recognition on Kinetics-100
1 code implementation • CVPR 2021 • Hongguang Zhang, Piotr Koniusz, Songlei Jian, Hongdong Li, Philip H. S. Torr
The majority of existing few-shot learning methods describe image relations with binary labels.
no code implementations • 6 Jan 2020 • Hongguang Zhang, Philip H. S. Torr, Piotr Koniusz
In this paper, we study the impact of scale and location mismatch in the few-shot learning scenario, and propose a novel Spatially-aware Matching (SM) scheme to effectively perform matching across multiple scales and locations, and learn image relations by giving the highest weights to the best matching pairs.
1 code implementation • CVPR 2019 • Hongguang Zhang, Yuchao Dai, Hongdong Li, Piotr Koniusz
depth, we propose a stacked version of our multi-patch model.
Ranked #9 on Deblurring on RealBlur-R (trained on GoPro) (SSIM (sRGB) metric)
no code implementations • CVPR 2019 • Hongguang Zhang, Jing Zhang, Piotr Koniusz
To the best of our knowledge, we are the first to leverage saliency maps for such a task and we demonstrate their usefulness in hallucinating additional datapoints for few-shot learning.
no code implementations • 10 Nov 2018 • Hongguang Zhang, Piotr Koniusz
In this paper, we propose a similarity learning network leveraging second-order information and Power Normalizations.
no code implementations • 8 Nov 2018 • Hongguang Zhang, Piotr Koniusz
Specifically, we leverage two sources of datapoints (observed and auxiliary) to train some classifier to recognize which test datapoints come from seen and which from unseen classes.
Generalized Zero-Shot Learning Generative Adversarial Network +1
no code implementations • ECCV 2018 • Piotr Koniusz, Yusuf Tas, Hongguang Zhang, Mehrtash Harandi, Fatih Porikli, Rui Zhang
To achieve robust baselines, we build on a recent approach that aligns per-class scatter matrices of the source and target CNN streams.
no code implementations • CVPR 2018 • Piotr Koniusz, Hongguang Zhang, Fatih Porikli
In this paper, we reconsider these operators in the deep learning setup by introducing a novel layer that implements PN for non-linear pooling of feature maps.
no code implementations • CVPR 2018 • Hongguang Zhang, Piotr Koniusz
In contrast, we apply well-established kernel methods to learn a non-linear mapping between the feature and attribute spaces.
no code implementations • 4 Feb 2018 • Piotr Koniusz, Yusuf Tas, Hongguang Zhang, Mehrtash Harandi, Fatih Porikli, Rui Zhang
To achieve robust baselines, we build on a recent approach that aligns per-class scatter matrices of the source and target CNN streams [15].
no code implementations • 25 Jul 2017 • Changbo Fu, Xiaopeng Zhou, Xun Chen, Yunhua Chen, Xiangyi Cui, Deqing Fang, Karl Giboni, Franco Giuliani, Ke Han, Xingtao Huang, Xiangdong Ji, Yonglin Ju, Siao Lei, Shaoli Li, Huaxuan Liu, Jianglai Liu, Yugang Ma, Yajun Mao, Xiangxiang Ren, Andi Tan, Hongwei Wang, Jimin Wang, Meng Wang, Qiuhong Wang, Siguang Wang, Xuming Wang, Zhou Wang, Shiyong Wu, Mengjiao Xiao, Pengwei Xie, Binbin Yan, Yong Yang, Jianfeng Yue, Hongguang Zhang, Tao Zhang, Li Zhao, Ning Zhou
We report new searches for the solar axions and galactic axion-like dark matter particles, using the first low-background data from PandaX-II experiment at China Jinping Underground Laboratory, corresponding to a total exposure of about $2. 7\times 10^4$ kg$\cdot$day.
High Energy Physics - Experiment Solar and Stellar Astrophysics High Energy Physics - Phenomenology