Search Results for author: Ran Cheng

Found 37 papers, 10 papers with code

Accelerating Vision-Language Pretraining with Free Language Modeling

1 code implementation24 Mar 2023 Teng Wang, Yixiao Ge, Feng Zheng, Ran Cheng, Ying Shan, XiaoHu Qie, Ping Luo

FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted.

Language Modelling Masked Language Modeling

Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos

no code implementations11 Mar 2023 Teng Wang, Jinrui Zhang, Feng Zheng, Wenhao Jiang, Ran Cheng, Ping Luo

TEG learns to adaptively ground the possible event proposals given a set of sentences by estimating the cross-modal distance in a joint semantic space.

Dense Video Captioning Text Generation

Evolutionary Reinforcement Learning: A Survey

no code implementations7 Mar 2023 Hui Bai, Ran Cheng, Yaochu Jin

This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL).

Board Games Hyperparameter Optimization +3

EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation

1 code implementation29 Jan 2023 Beichen Huang, Ran Cheng, Yaochu Jin, Kay Chen Tan

Second, we design a scalable computing framework for running EC algorithms on distributed GPU devices.

OpenAI Gym

Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games

no code implementations21 Sep 2022 Hui Bai, Ruimin Shen, Yue Lin, Botian Xu, Ran Cheng

In comparison with the state-of-the-art RLlib, we empirically demonstrate the unique advantages of Lamarckian on benchmark tests with up to 6000 CPU cores: i) both the sampling efficiency and training speed are doubled when running PPO on Google football game; ii) the training speed is 13 times faster when running PBT+PPO on Pong game.

Distributed Computing reinforcement-learning +1

Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation

no code implementations14 Aug 2022 Zhichao Lu, Ran Cheng, Shihua Huang, Haoming Zhang, Changxiao Qiu, Fan Yang

The main challenges of applying NAS to semantic segmentation arise from two aspects: (i) high-resolution images to be processed; (ii) additional requirement of real-time inference speed (i. e., real-time semantic segmentation) for applications such as autonomous driving.

Autonomous Driving Image Classification +2

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

no code implementations8 Aug 2022 Zhichao Lu, Ran Cheng, Yaochu Jin, Kay Chen Tan, Kalyanmoy Deb

From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them.

Multiobjective Optimization Neural Architecture Search

Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures

no code implementations12 Jul 2022 Jia Liu, Ran Cheng, Yaochu Jin

First, we formulate the NAS problem for enhancing adversarial robustness of deep neural networks into a multiobjective optimization problem.

Adversarial Robustness Multiobjective Optimization +1

Exploiting Context Information for Generic Event Boundary Captioning

1 code implementation3 Jul 2022 Jinrui Zhang, Teng Wang, Feng Zheng, Ran Cheng, Ping Luo

Previous methods only process the information of a single boundary at a time, which lacks utilization of video context information.

Boundary Captioning

VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix

1 code implementation17 Jun 2022 Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo

Existing vision-language pre-training (VLP) methods primarily rely on paired image-text datasets, which are either annotated by enormous human labors, or crawled from the internet followed by elaborate data cleaning techniques.

Contrastive Learning Data Augmentation +1

Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision

no code implementations19 Apr 2022 Yueming Li, Ying Jiang, Lu Lan, Xiaowei Ge, Ran Cheng, Yuewei Zhan, Guo Chen, Linli Shi, Runyu Wang, Nan Zheng, Chen Yang, Ji-Xin Cheng

Here, we report optically-generated focused ultrasound (OFUS) for non-invasive brain stimulation with ultrahigh precision.

Semantic-Aware Pretraining for Dense Video Captioning

no code implementations13 Apr 2022 Teng Wang, Zhu Liu, Feng Zheng, Zhichao Lu, Ran Cheng, Ping Luo

This report describes the details of our approach for the event dense-captioning task in ActivityNet Challenge 2021.

Dense Captioning Dense Video Captioning

R4: A Framework for Route Representation and Route Recommendation

no code implementations20 Oct 2021 Ran Cheng, Chao Chen, Longfei Xu, Shen Li, Lei Wang, Hengbin Cui, Kaikui Liu, Xiaolong Li

For user representation, we utilize a series of historical navigation to extract user preference.

Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation

no code implementations8 Oct 2021 Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu, Jing Wang, Miao Zhang

For the goal of automated design of high-performance deep convolutional neural networks (CNNs), Neural Architecture Search (NAS) methodology is becoming increasingly important for both academia and industries. Due to the costly stochastic gradient descent (SGD) training of CNNs for performance evaluation, most existing NAS methods are computationally expensive for real-world deployments.

Neural Architecture Search

Revisiting Self-Training for Few-Shot Learning of Language Model

1 code implementation EMNLP 2021 Yiming Chen, Yan Zhang, Chen Zhang, Grandee Lee, Ran Cheng, Haizhou Li

In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM.

Benchmarking Few-Shot Learning +4

GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network

no code implementations ICCV 2021 Ryan Razani, Ran Cheng, Enxu Li, Ehsan Taghavi, Yuan Ren, Liu Bingbing

GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information.

Panoptic Segmentation

Photoacoustic Silk Scaffolds for Neural stimulation and Regeneration

no code implementations28 Jun 2021 Nan Zheng, Vincent Fitzpatrick, Ran Cheng, Linli Shi, David L. Kaplan, Chen Yang

We also confirmed that photoacoustic neural stimulation promoted neurite outgrowth by impacting the brain-derived neurotrophic factor (BDNF) pathway.

S3Net: 3D LiDAR Sparse Semantic Segmentation Network

no code implementations15 Mar 2021 Ran Cheng, Ryan Razani, Yuan Ren, Liu Bingbing

In literature, several approaches are introduced to attempt LiDAR semantic segmentation task, such as projection-based (range-view or birds-eye-view), and voxel-based approaches.

Autonomous Driving LIDAR Semantic Segmentation +1

Spin Nernst Effect of Antiferromagnetic Magnons in the Presence of Spin Diffusion

no code implementations11 Mar 2021 Hantao Zhang, Ran Cheng

Magnon spin Nernst effect was recently proposed as an intrinsic effect in antiferromagnets, where spin diffusion and boundary spin transmission have been ignored.

Mesoscale and Nanoscale Physics Materials Science Other Condensed Matter

(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network

no code implementations8 Feb 2021 Ran Cheng, Ryan Razani, Ehsan Taghavi, Enxu Li, Bingbing Liu

Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority.

3D Semantic Segmentation LIDAR Semantic Segmentation +1

S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

no code implementations16 Dec 2020 Ran Cheng, Christopher Agia, Yuan Ren, Xinhai Li, Liu Bingbing

With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike.

3D Semantic Scene Completion Semantic Segmentation

Multi-objective Neural Architecture Search with Almost No Training

no code implementations27 Nov 2020 Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu

In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries.

Neural Architecture Search Transfer Learning

RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning

2 code implementations14 Sep 2020 Hao Tan, Ran Cheng, Shihua Huang, Cheng He, Changxiao Qiu, Fan Yang, Ping Luo

Despite the remarkable successes of Convolutional Neural Networks (CNNs) in computer vision, it is time-consuming and error-prone to manually design a CNN.

Keypoint Detection Neural Architecture Search +3

SoloGAN: Multi-domain Multimodal Unpaired Image-to-Image Translation via a Single Generative Adversarial Network

no code implementations4 Aug 2020 Shihua Huang, Cheng He, Ran Cheng

Existing I2I translation methods adopt multiple domain-specific content encoders for different domains, where each domain-specific content encoder is trained with images from the same domain only.

Image-to-Image Translation Translation

Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction

no code implementations10 Mar 2020 Yan Xiao, Yaochu Jin, Ran Cheng, Kuangrong Hao

With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information.

Relation Extraction

Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search

no code implementations7 Mar 2020 Haoyu Zhang, Yaochu Jin, Ran Cheng, Kuangrong Hao

Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms.

Neural Architecture Search

Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs)

no code implementations11 Oct 2019 Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin

The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables.

PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks

no code implementations11 Sep 2019 Hang Yu, Aishan Liu, Xianglong Liu, Gengchao Li, Ping Luo, Ran Cheng, Jichen Yang, Chongzhi Zhang

In other words, DNNs trained with PDA are able to obtain more robustness against both adversarial attacks as well as common corruptions than the recent state-of-the-art methods.

Data Augmentation

Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks

no code implementations10 Jul 2019 Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin

Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i. e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.

Multiobjective Test Problems with Degenerate Pareto Fronts

no code implementations7 Jun 2018 Liangli Zhen, Miqing Li, Ran Cheng, Dezhong Peng, Xin Yao

The redundancy of some objectives can lead to the multiobjective problem having a degenerate Pareto front, i. e., the dimension of the Pareto front of the $m$-objective problem be less than (m-1).

Multiobjective Optimization

PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization

no code implementations4 Jan 2017 Ye Tian, Ran Cheng, Xingyi Zhang, Yaochu Jin

To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators.

Multiobjective Optimization

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