Search Results for author: Xiangyang Ji

Found 58 papers, 20 papers with code

Integral Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection

no code implementations19 May 2022 Xiaosong Zhang, Feng Liu, Zhiliang Peng, Zonghao Guo, Fang Wan, Xiangyang Ji, Qixiang Ye

However, except for the backbone networks, other detector components, such as the detector head and the feature pyramid network, remain randomly initialized, which hinders the consistency between detectors and pre-trained models.

Few-Shot Object Detection

PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI

1 code implementation24 Apr 2022 Jingfen Xie, Jian Zhang, Yongbing Zhang, Xiangyang Ji

Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i. e., where to sample and how to reconstruct.


Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation

1 code implementation18 Apr 2022 Wenbo Zhao, Xianming Liu, Zhiwei Zhong, Junjun Jiang, Wei Gao, Ge Li, Xiangyang Ji

Most existing methods either take the end-to-end supervised learning based manner, where large amounts of pairs of sparse input and dense ground-truth are exploited as supervision information; or treat up-scaling of different scale factors as independent tasks, and have to build multiple networks to handle upsampling with varying factors.

Self-Supervised Learning

Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies

no code implementations24 Mar 2022 Zihan Zhang, Xiangyang Ji, Simon S. Du

This paper gives the first polynomial-time algorithm for tabular Markov Decision Processes (MDP) that enjoys a regret bound \emph{independent on the planning horizon}.


GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting

1 code implementation15 Mar 2022 Yan Di, Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xiangyang Ji, Nassir Navab, Federico Tombari

While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications.

6D Pose Estimation using RGB

Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

1 code implementation8 Mar 2022 Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji

A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector.

Adversarial Attack Traffic Sign Recognition

Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN

1 code implementation24 Jan 2022 Bo Li, Qiulin Wang, JiQuan Pei, Yu Yang, Xiangyang Ji

First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e. g., face parsers and face landmark detectors.

Counterfactual Explanation Disentanglement +2

Towards End-to-End Image Compression and Analysis with Transformers

1 code implementation17 Dec 2021 Yuanchao Bai, Xu Yang, Xianming Liu, Junjun Jiang, YaoWei Wang, Xiangyang Ji, Wen Gao

Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction.

Classification Image Classification +2

Deep Attentional Guided Image Filtering

1 code implementation13 Dec 2021 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Xiangyang Ji

Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images.

Collaborative Filtering Depth Image Upsampling +1

Wasserstein Unsupervised Reinforcement Learning

no code implementations15 Oct 2021 Shuncheng He, Yuhang Jiang, Hongchang Zhang, Jianzhun Shao, Xiangyang Ji

These pre-trained policies can accelerate learning when endowed with external reward, and can also be used as primitive options in hierarchical reinforcement learning.

Hierarchical Reinforcement Learning reinforcement-learning +1

Learning to Annotate Part Segmentation with Gradient Matching

no code implementations ICLR 2022 Yu Yang, Xiaotian Cheng, Hakan Bilen, Xiangyang Ji

In particular, we formulate the annotator learning as the following learning-to-learn problem.

Learning Towards The Largest Margins

no code implementations ICLR 2022 Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji

Specifically, we firstly propose to employ the class margin as the measure of inter-class separability, and the sample margin as the measure of intra-class compactness.

Face Verification imbalanced classification +1

Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data

no code implementations23 Sep 2021 Jialei Xu, Yuanchao Bai, Xianming Liu, Junjun Jiang, Xiangyang Ji

In this paper, we propose a novel weakly-supervised framework to train a monocular depth estimation network to generate HR depth maps with resolution-mismatched supervision, i. e., the inputs are HR color images and the ground-truth are low-resolution (LR) depth maps.

Monocular Depth Estimation

SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation

2 code implementations ICCV 2021 Yan Di, Fabian Manhardt, Gu Wang, Xiangyang Ji, Nassir Navab, Federico Tombari

Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (e. g. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging problem.

6D Pose Estimation 6D Pose Estimation using RGB +1

Learning with Noisy Labels via Sparse Regularization

1 code implementation ICCV 2021 Xiong Zhou, Xianming Liu, Chenyang Wang, Deming Zhai, Junjun Jiang, Xiangyang Ji

In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector.

Learning with noisy labels

Physics-Based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging

no code implementations CVPR 2021 Feilong Zhang, Xianming Liu, Cheng Guo, Shiyi Lin, Junjun Jiang, Xiangyang Ji

Specifically, we unfold the iterative process of the alternative projection phase retrieval into a feed-forward neural network, whose layers mimic the processing flow.

Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression

no code implementations CVPR 2021 Yuanchao Bai, Xianming Liu, WangMeng Zuo, YaoWei Wang, Xiangyang Ji

To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.

Image Compression

Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation

1 code implementation CVPR 2021 Binghao Liu, Yao Ding, Jianbin Jiao, Xiangyang Ji, Qixiang Ye

Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples.

Few-Shot Semantic Segmentation Semantic Segmentation

Multiple instance active learning for object detection

1 code implementation CVPR 2021 Tianning Yuan, Fang Wan, Mengying Fu, Jianzhuang Liu, Songcen Xu, Xiangyang Ji, Qixiang Ye

Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection.

Active Object Detection Multiple Instance Learning +1

High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion

no code implementations4 Apr 2021 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Zhiwen Chen, Xiangyang Ji

Specifically, to effectively extract and combine relevant information from LR depth and HR guidance, we propose a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhance block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics.

Depth Map Super-Resolution

Learning Foreground-Background Segmentation from Improved Layered GANs

no code implementations1 Apr 2021 Yu Yang, Hakan Bilen, Qiran Zou, Wing Yin Cheung, Xiangyang Ji

Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task.

Semantic Segmentation Unsupervised Object Segmentation

Learning Scalable $\ell_\infty$-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression

no code implementations31 Mar 2021 Yuanchao Bai, Xianming Liu, WangMeng Zuo, YaoWei Wang, Xiangyang Ji

To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.

Image Compression

Reducing Conservativeness Oriented Offline Reinforcement Learning

no code implementations27 Feb 2021 Hongchang Zhang, Jianzhun Shao, Yuhang Jiang, Shuncheng He, Xiangyang Ji

In offline reinforcement learning, a policy learns to maximize cumulative rewards with a fixed collection of data.


Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning

no code implementations24 Feb 2021 Jianzhun Shao, Hongchang Zhang, Yuhang Jiang, Shuncheng He, Xiangyang Ji

Reward decomposition is a critical problem in centralized training with decentralized execution~(CTDE) paradigm for multi-agent reinforcement learning.

Meta-Learning Multi-agent Reinforcement Learning +3

GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

1 code implementation CVPR 2021 Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji

In this work, we perform an in-depth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations.

6D Pose Estimation 6D Pose Estimation using RGB

Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP

no code implementations NeurIPS 2021 Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du

With the new confidence sets, we obtain the follow regret bounds: For linear bandits, we obtain an $\tilde{O}(poly(d)\sqrt{1 + \sum_{k=1}^{K}\sigma_k^2})$ data-dependent regret bound, where $d$ is the feature dimension, $K$ is the number of rounds, and $\sigma_k^2$ is the \emph{unknown} variance of the reward at the $k$-th round.

Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition

no code implementations NeurIPS 2020 Zihan Zhang, Yuan Zhou, Xiangyang Ji

We study the reinforcement learning problem in the setting of finite-horizon1episodic Markov Decision Processes (MDPs) with S states, A actions, and episode length H. We propose a model-free algorithm UCB-ADVANTAGE and prove that it achieves \tilde{O}(\sqrt{H^2 SAT}) regret where T=KH and K is the number of episodes to play.


Nearly Minimax Optimal Reward-free Reinforcement Learning

no code implementations12 Oct 2020 Zihan Zhang, Simon S. Du, Xiangyang Ji

In the planning phase, the agent needs to return a near-optimal policy for arbitrary reward functions.


Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon

no code implementations28 Sep 2020 Zihan Zhang, Xiangyang Ji, Simon S. Du

Episodic reinforcement learning generalizes contextual bandits and is often perceived to be more difficult due to long planning horizon and unknown state-dependent transitions.

Decision Making Multi-Armed Bandits +1

Robust RGB-based 6-DoF Pose Estimation without Real Pose Annotations

no code implementations19 Aug 2020 Zhigang Li, Yinlin Hu, Mathieu Salzmann, Xiangyang Ji

We achieve state of the art performance on LINEMOD, and OccludedLINEMOD in without real-pose setting, even outperforming methods that rely on real annotations during training on Occluded-LINEMOD.

Pose Estimation

Depth image denoising using nuclear norm and learning graph model

no code implementations9 Aug 2020 Chenggang Yan, Zhisheng Li, Yongbing Zhang, Yutao Liu, Xiangyang Ji, Yongdong Zhang

The depth images denoising are increasingly becoming the hot research topic nowadays because they reflect the three-dimensional (3D) scene and can be applied in various fields of computer vision.

Image Denoising Image Restoration

Domain Contrast for Domain Adaptive Object Detection

no code implementations26 Jun 2020 Feng Liu, Xiaoxong Zhang, Fang Wan, Xiangyang Ji, Qixiang Ye

We present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors.

Contrastive Learning Object Detection

Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity

no code implementations6 Jun 2020 Zihan Zhang, Yuan Zhou, Xiangyang Ji

In this paper we consider the problem of learning an $\epsilon$-optimal policy for a discounted Markov Decision Process (MDP).


Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition

no code implementations21 Apr 2020 Zihan Zhang, Yuan Zhou, Xiangyang Ji

We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states, $A$ actions, and episode length $H$.


PgNN: Physics-guided Neural Network for Fourier Ptychographic Microscopy

no code implementations19 Sep 2019 Yongbing Zhang, Yangzhe Liu, Xiu Li, Shaowei Jiang, Krishna Dixit, Xinfeng Zhang, Xiangyang Ji

Since the optimal parameters of the PgNN can be derived by minimizing the difference between the model-generated images and real captured angle-varied images corresponding to the same scene, the proposed PgNN can get rid of the problem of massive training data as in traditional supervised methods.

Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function

no code implementations NeurIPS 2019 Zihan Zhang, Xiangyang Ji

We present an algorithm based on the \emph{Optimism in the Face of Uncertainty} (OFU) principle which is able to learn Reinforcement Learning (RL) modeled by Markov decision process (MDP) with finite state-action space efficiently.


C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection

1 code implementation CVPR 2019 Fang Wan, Chang Liu, Wei Ke, Xiangyang Ji, Jianbin Jiao, Qixiang Ye

Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors.

Multiple Instance Learning Weakly Supervised Object Detection +1

Bi-stream Pose Guided Region Ensemble Network for Fingertip Localization from Stereo Images

no code implementations26 Feb 2019 Guijin Wang, Cairong Zhang, Xinghao Chen, Xiangyang Ji, Jing-Hao Xue, Hang Wang

To mitigate these limitations and promote further research on hand pose estimation from stereo images, we propose a new large-scale binocular hand pose dataset called THU-Bi-Hand, offering a new perspective for fingertip localization.

3D Hand Pose Estimation

DeepIM: Deep Iterative Matching for 6D Pose Estimation

1 code implementation ECCV 2018 Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, Dieter Fox

Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality.

6D Pose Estimation 6D Pose Estimation using RGB

Dynamic Filtering with Large Sampling Field for ConvNets

no code implementations ECCV 2018 Jialin Wu, Dai Li, Yu Yang, Chandrajit Bajaj, Xiangyang Ji

We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbor regions.

Object Detection Semantic Segmentation +1

A Graphical Social Topology Model for Multi-Object Tracking

no code implementations14 Feb 2017 Shan Gao, Xiaogang Chen, Qixiang Ye, Junliang Xing, Arjan Kuijper, Xiangyang Ji

Inspired with the social affinity property of moving objects, we propose a Graphical Social Topology (GST) model, which estimates the group dynamics by jointly modeling the group structure and the states of objects using a topological representation.

Multi-Object Tracking

Action Recognition with Joint Attention on Multi-Level Deep Features

no code implementations9 Jul 2016 Jialin Wu, Gu Wang, Wukui Yang, Xiangyang Ji

We propose a novel deep supervised neural network for the task of action recognition in videos, which implicitly takes advantage of visual tracking and shares the robustness of both deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).

Action Recognition Action Recognition In Videos +2

Fast and High Quality Highlight Removal from A Single Image

no code implementations1 Dec 2015 Dongsheng An, Jinli Suo, Xiangyang Ji, Haoqian Wang, Qionghai Dai

Specifically, this paper derives a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively.

Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking

no code implementations28 Nov 2015 Qi Guo, Le Dan, Dong Yin, Xiangyang Ji

Multi-object tracking remains challenging due to frequent occurrence of occlusions and outliers.

Multi-Object Tracking

Efficient Divide-And-Conquer Classification Based on Feature-Space Decomposition

no code implementations29 Jan 2015 Qi Guo, Bo-Wei Chen, Feng Jiang, Xiangyang Ji, Sun-Yuan Kung

Firstly, we divide the feature space into several subspaces using the decomposition method proposed in this paper.

General Classification

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