Search Results for author: Shuo Chen

Found 27 papers, 16 papers with code

Decision-making with Imaginary Opponent Models

no code implementations22 Nov 2022 Jing Sun, Shuo Chen, Cong Zhang, Jie Zhang

Specifically, the actor maintains a speculated belief of the opponents, which we call the \textit{imaginary opponent models}, to predict opponents' actions using local observations and makes decisions accordingly.

Decision Making

R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition

no code implementations20 Nov 2022 Shuo Chen, Tan Yu, Ping Li

Recently, vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community.

3D Object Recognition Image Classification +1

IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis

1 code implementation2 Oct 2022 Weicai Ye, Shuo Chen, Chong Bao, Hujun Bao, Marc Pollefeys, Zhaopeng Cui, Guofeng Zhang

Existing inverse rendering combined with neural rendering methods~\cite{zhang2021physg, zhang2022modeling} can only perform editable novel view synthesis on object-specific scenes, while we present intrinsic neural radiance fields, dubbed IntrinsicNeRF, which introduce intrinsic decomposition into the NeRF-based~\cite{mildenhall2020nerf} neural rendering method and can extend its application to room-scale scenes.

Neural Rendering Novel View Synthesis

Gradient Norm Minimization of Nesterov Acceleration: $o(1/k^3)$

no code implementations19 Sep 2022 Shuo Chen, Bin Shi, Ya-xiang Yuan

In the history of first-order algorithms, Nesterov's accelerated gradient descent (NAG) is one of the milestones.

Higher-order accurate two-sample network inference and network hashing

1 code implementation16 Aug 2022 Meijia Shao, Dong Xia, Yuan Zhang, Qiong Wu, Shuo Chen

Our method extends the classical two-sample t-test to the network setting.

PVO: Panoptic Visual Odometry

1 code implementation4 Jul 2022 Weicai Ye, Xinyue Lan, Shuo Chen, Yuhang Ming, Xingyuan Yu, Hujun Bao, Zhaopeng Cui, Guofeng Zhang

PVO models visual odometry (VO) and video panoptic segmentation (VPS) in a unified view, enabling the two tasks to facilitate each other.

Optical Flow Estimation Panoptic Segmentation +3

Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation

1 code implementation CVPR 2022 Jinchao Yang, Fei Guo, Shuo Chen, Jun Li, Jian Yang

Given a source product, a target product, and an art style image, our method produces a neural warping field that warps the source shape to imitate the geometric style of the target and a neural texture transformation network that transfers the artistic style to the warped source product.

Style Transfer

MVT: Multi-view Vision Transformer for 3D Object Recognition

no code implementations25 Oct 2021 Shuo Chen, Tan Yu, Ping Li

Nevertheless, multi-view CNN models cannot model the communications between patches from different views, limiting its effectiveness in 3D object recognition.

3D Object Recognition Inductive Bias

Diagnosing Errors in Video Relation Detectors

1 code implementation25 Oct 2021 Shuo Chen, Pascal Mettes, Cees G. M. Snoek

Video relation detection forms a new and challenging problem in computer vision, where subjects and objects need to be localized spatio-temporally and a predicate label needs to be assigned if and only if there is an interaction between the two.

Action Localization object-detection +1

Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution

no code implementations NeurIPS 2021 Guangpin Tao, Xiaozhong Ji, Wenzhuo Wang, Shuo Chen, Chuming Lin, Yun Cao, Tong Lu, Donghao Luo, Ying Tai

In this paper, we propose a novel blind SR framework to super-resolve LR images degraded by arbitrary blur kernel with accurate kernel estimation in frequency domain.

Image Super-Resolution Translation

Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?

1 code implementation20 Sep 2021 Deqiang Li, Tian Qiu, Shuo Chen, Qianmu Li, Shouhuai Xu

Our main findings are: (i) predictive uncertainty indeed helps achieve reliable malware detection in the presence of dataset shift, but cannot cope with adversarial evasion attacks; (ii) approximate Bayesian methods are promising to calibrate and generalize malware detectors to deal with dataset shift, but cannot cope with adversarial evasion attacks; (iii) adversarial evasion attacks can render calibration methods useless, and it is an open problem to quantify the uncertainty associated with the predicted labels of adversarial examples (i. e., it is not effective to use predictive uncertainty to detect adversarial examples).

Android Malware Detection Malware Detection

Social Fabric: Tubelet Compositions for Video Relation Detection

1 code implementation ICCV 2021 Shuo Chen, Zenglin Shi, Pascal Mettes, Cees G. M. Snoek

We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives.

Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model

1 code implementation NeurIPS 2021 Jiangning Zhang, Chao Xu, Jian Li, Wenzhou Chen, Yabiao Wang, Ying Tai, Shuo Chen, Chengjie Wang, Feiyue Huang, Yong liu

Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them have consistent mathematical representation.

Image Retrieval Retrieval

Contrastive Embedding for Generalized Zero-Shot Learning

3 code implementations CVPR 2021 Zongyan Han, ZhenYong Fu, Shuo Chen, Jian Yang

To tackle this issue, we propose to integrate the generation model with the embedding model, yielding a hybrid GZSL framework.

Generalized Zero-Shot Learning

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

7 code implementations NeurIPS 2020 Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang

Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations.

Dense Object Detection General Classification

Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction

1 code implementation22 Feb 2020 Jiangpeng Yan, Shuo Chen, Yongbing Zhang, Xiu Li

Our proposed method can reach a better trade-off between computation cost and reconstruction performance for MR reconstruction problem with good generalizability and offer insights to design neural networks for other medical image applications.

Image Reconstruction Neural Architecture Search +1

Curvilinear Distance Metric Learning

1 code implementation NeurIPS 2019 Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang

To address this issue, we first reveal that the traditional linear distance metric is equivalent to the cumulative arc length between the data pair's nearest points on the learned straight measurer lines.

Metric Learning

GANSynth: Adversarial Neural Audio Synthesis

5 code implementations ICLR 2019 Jesse Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts

Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence.

Audio Generation

Formal Specification and Verification of Smart Contracts for Azure Blockchain

1 code implementation20 Dec 2018 Shuvendu K. Lahiri, Shuo Chen, Yuepeng Wang, Isil Dillig

In this paper, we describe the formal verification of Smart Contracts offered as part of the Azure Blockchain Content and Samples on github.

Programming Languages F.3.1

Adversarial Metric Learning

no code implementations9 Feb 2018 Shuo Chen, Chen Gong, Jian Yang, Xiang Li, Yang Wei, Jun Li

In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs.

Metric Learning

Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift

4 code implementations CVPR 2019 Xiang Li, Shuo Chen, Xiaolin Hu, Jian Yang

Theoretically, we find that Dropout would shift the variance of a specific neural unit when we transfer the state of that network from train to test.

Deep Multi-Species Embedding

no code implementations28 Sep 2016 Di Chen, Yexiang Xue, Shuo Chen, Daniel Fink, Carla Gomes

Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling.

Cannot find the paper you are looking for? You can Submit a new open access paper.