Search Results for author: Feng Liang

Found 34 papers, 11 papers with code

Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline

1 code implementation29 Jan 2023 Yangguang Li, Bin Huang, Zeren Chen, Yufeng Cui, Feng Liang, Mingzhu Shen, Fenggang Liu, Enze Xie, Lu Sheng, Wanli Ouyang, Jing Shao

Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image feature to 3D voxel space, (2) an multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference.

Data Augmentation

Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perception

1 code implementation19 Jan 2023 Bin Huang, Yangguang Li, Enze Xie, Feng Liang, Luya Wang, Mingzhu Shen, Fenggang Liu, Tianqi Wang, Ping Luo, Jing Shao

Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving.

Autonomous Driving Data Augmentation

MobileTL: On-device Transfer Learning with Inverted Residual Blocks

no code implementations5 Dec 2022 Hung-Yueh Chiang, Natalia Frumkin, Feng Liang, Diana Marculescu

MobileTL trains the shifts for internal normalization layers to avoid storing activation maps for the backward pass.

Transfer Learning

Learned Image Compression with Generalized Octave Convolution and Cross-Resolution Parameter Estimation

no code implementations7 Sep 2022 Haisheng Fu, Feng Liang

In addition, these methods based on the context-adaptive entropy model cannot be accelerated in the decoding process by parallel computing devices, e. g. FPGA or GPU.

Image Compression MS-SSIM +1

Play It Cool: Dynamic Shifting Prevents Thermal Throttling

no code implementations22 Jun 2022 Yang Zhou, Feng Liang, Ting-Wu Chin, Diana Marculescu

Machine learning (ML) has entered the mobile era where an enormous number of ML models are deployed on edge devices.

Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering

no code implementations21 Jun 2022 Haisheng Fu, Feng Liang, Jie Liang, Binglin Li, Guohe Zhang, Jingning Han

Based on this observation, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only needs one stage of MSRB to yield satisfactory reconstruction, thereby reducing the decoding complexity without sacrifcing performance.

Image Compression Quantization

SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners

2 code implementations28 May 2022 Feng Liang, Yangguang Li, Diana Marculescu

The proposed Supervised MAE (SupMAE) only exploits a visible subset of image patches for classification, unlike the standard supervised pre-training where all image patches are used.

Representation Learning Transfer Learning

Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization

no code implementations8 May 2022 Ngan Nguyen, Feng Liang, Dominik Engel, Ciril Bohak, Peter Wonka, Timo Ropinski, Ivan Viola

We propose a new microscopy simulation system that can depict atomistic models in a micrograph visual style, similar to results of physical electron microscopy imaging.


Personalized Execution Time Optimization for the Scheduled Jobs

no code implementations11 Mar 2022 Yang Liu, Juan Wang, Zhengxing Chen, Ian Fox, Imani Mufti, Jason Sukumaran, Baokun He, Xiling Sun, Feng Liang

Scheduled batch jobs have been widely used on the asynchronous computing platforms to execute various enterprise applications, including the scheduled notifications and the candidate pre-computation for the modern recommender systems.

Learning-To-Rank Recommendation Systems +1

Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision

1 code implementation11 Mar 2022 Yufeng Cui, Lichen Zhao, Feng Liang, Yangguang Li, Jing Shao

This is because researchers do not choose consistent training recipes and even use different data, hampering the fair comparison between different methods.

RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training

no code implementations18 Jan 2022 Luya Wang, Feng Liang, Yangguang Li, Honggang Zhang, Wanli Ouyang, Jing Shao

Recently, self-supervised vision transformers have attracted unprecedented attention for their impressive representation learning ability.

Contrastive Learning Representation Learning

Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

2 code implementations ICLR 2022 Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, Junjie Yan

Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks.

Zero-Shot Learning

Learning Topic Models: Identifiability and Finite-Sample Analysis

no code implementations8 Oct 2021 Yinyin Chen, Shishuang He, Yun Yang, Feng Liang

Our theory introduces a new set of geometric conditions for topic model identifiability, conditions that are weaker than conventional separability conditions, which typically rely on the existence of pure topic documents or of anchor words.

Topic Models

Inception Convolution with Efficient Dilation Search

1 code implementation CVPR 2021 Jie Liu, Chuming Li, Feng Liang, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang, Dong Xu

To develop a practical method for learning complex inception convolution based on the data, a simple but effective search algorithm, referred to as efficient dilation optimization (EDO), is developed.

Human Detection Instance Segmentation +4

Reinforcement Learning-based Product Delivery Frequency Control

no code implementations20 Dec 2020 Yang Liu, Zhengxing Chen, Kittipat Virochsiri, Juan Wang, Jiahao Wu, Feng Liang

We demonstrate statistically significant improvement in daily metrics and resource efficiency by our method in several notification applications at a scale of billions of users.

Recommendation Systems reinforcement-learning +1

NASGEM: Neural Architecture Search via Graph Embedding Method

no code implementations8 Jul 2020 Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang, Shi-Yu Li, Feng Liang, Feng Yan, Meng Li, Vikas Chandra, Hai Li, Yiran Chen

To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method.

Graph Embedding Graph Similarity +3

Computation Reallocation for Object Detection

no code implementations ICLR 2020 Feng Liang, Chen Lin, Ronghao Guo, Ming Sun, Wei Wu, Junjie Yan, Wanli Ouyang

However, classification allocation pattern is usually adopted directly to object detector, which is proved to be sub-optimal.

Instance Segmentation Neural Architecture Search +3

Bayesian Joint Estimation of Multiple Graphical Models

1 code implementation NeurIPS 2019 Lingrui Gan, Xinming Yang, Naveen Narisetty, Feng Liang

In this paper, we propose a novel Bayesian group regularization method based on the spike and slab Lasso priors for jointly estimating multiple graphical models.

A Deep Image Compression Framework for Face Recognition

no code implementations3 Jul 2019 Nai Bian, Feng Liang, Haisheng Fu, Bo Lei

In this paper, we propose a deep convolutional autoencoder compression network for face recognition tasks.

Face Recognition Face Verification +1

Bayesian Regularization for Graphical Models with Unequal Shrinkage

no code implementations6 May 2018 Lingrui Gan, Naveen N. Narisetty, Feng Liang

We consider a Bayesian framework for estimating a high-dimensional sparse precision matrix, in which adaptive shrinkage and sparsity are induced by a mixture of Laplace priors.

Discriminative Similarity for Clustering and Semi-Supervised Learning

no code implementations5 Sep 2017 Yingzhen Yang, Feng Liang, Nebojsa Jojic, Shuicheng Yan, Jiashi Feng, Thomas S. Huang

By generalization analysis via Rademacher complexity, the generalization error bound for the kernel classifier learned from hypothetical labeling is expressed as the sum of pairwise similarity between the data from different classes, parameterized by the weights of the kernel classifier.

An Empirical Bayes Approach for High Dimensional Classification

1 code implementation16 Feb 2017 Yunbo Ouyang, Feng Liang

We propose an empirical Bayes estimator based on Dirichlet process mixture model for estimating the sparse normalized mean difference, which could be directly applied to the high dimensional linear classification.

Classification General Classification

Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm

no code implementations25 Aug 2015 Daniel Khashabi, John Wieting, Jeffrey Yufei Liu, Feng Liang

Empirical studies have been carried out to compare our work with many constrained clustering algorithms from the literature on both a variety of data sets and under a variety of conditions such as using noisy side information and erroneous k values.

On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification

no code implementations NeurIPS 2014 Yingzhen Yang, Feng Liang, Shuicheng Yan, Zhangyang Wang, Thomas S. Huang

Modeling the underlying data distribution by nonparametric kernel density estimation, the generalization error bounds for both unsupervised nonparametric classifiers are the sum of nonparametric pairwise similarity terms between the data points for the purpose of clustering.

Density Estimation General Classification +1

PAC-Bayesian AUC classification and scoring

no code implementations NeurIPS 2014 James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang

We also extend our method to a class of non-linear score functions, essentially leading to a nonparametric procedure, by considering a Gaussian process prior.

Classification General Classification

Learning Locally-Adaptive Decision Functions for Person Verification

no code implementations CVPR 2013 Zhen Li, Shiyu Chang, Feng Liang, Thomas S. Huang, Liangliang Cao, John R. Smith

This paper proposes to learn a decision function for verification that can be viewed as a joint model of a distance metric and a locally adaptive thresholding rule.

Face Verification Metric Learning +2

Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models

no code implementations NeurIPS 2009 Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, Jiawei Han

First, we can boost the diversity of classification ensemble by incorporating multiple clustering outputs, each of which provides grouping constraints for the joint label predictions of a set of related objects.

General Classification

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