Search Results for author: Hongguang Zhang

Found 16 papers, 6 papers with code

Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring

1 code implementation14 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.

Deblurring

Multi-level Second-order Few-shot Learning

1 code implementation15 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.

Few-Shot action recognition Few Shot Action Recognition +2

Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification

no code implementations27 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).

Few-Shot Learning General Classification +3

Few-shot Action Recognition with Permutation-invariant Attention

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.

Few-Shot action recognition Few Shot Action Recognition +3

Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer

no code implementations6 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.

Deblurring Few-Shot Learning +2

Few-Shot Learning via Saliency-guided Hallucination of Samples

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.

Few-Shot Learning Hallucination

Power Normalizing Second-order Similarity Network for Few-shot Learning

no code implementations10 Nov 2018 Hongguang Zhang, Piotr Koniusz

In this paper, we propose a similarity learning network leveraging second-order information and Power Normalizations.

Few-Shot Learning Scene Recognition

Model Selection for Generalized Zero-shot Learning

no code implementations8 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 +2

A Deeper Look at Power Normalizations

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.

Material Classification Scene Recognition

Zero-Shot Kernel Learning

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.

Attribute Zero-Shot Learning

Museum Exhibit Identification Challenge for Domain Adaptation and Beyond

no code implementations4 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].

Domain Adaptation Few-Shot Learning

Limits on Axion Couplings from the first 80-day data of PandaX-II Experiment

no code implementations25 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

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