b'Shuchang Zhou'

research

∙ 08/10/2023

Proximal Policy Optimization Actual Combat: Manipulating Output Tokenizer Length

The Reinforcement Learning from Human Feedback (RLHF) plays a pivotal ro...

0 Miao Fan, et al. ∙

research

∙ 04/08/2023

PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures

Neural Radiance Fields (NeRF) have been widely adopted as practical and ...

0 Shuangkang Fang, et al. ∙

research

∙ 03/27/2023

UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View

In the field of 3D object detection for autonomous driving, the sensor p...

0 Shengchao Zhou, et al. ∙

research

∙ 03/17/2023

A Dynamic Multi-Scale Voxel Flow Network for Video Prediction

The performance of video prediction has been greatly boosted by advanced...

0 Xiaotao Hu, et al. ∙

research

∙ 03/13/2023

Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning

We propose universally slimmable self-supervised learning (dubbed as US3...

0 Yun-Hao Cao, et al. ∙

research

∙ 02/27/2023

OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion

3D Semantic Scene Completion (SSC) can provide dense geometric and seman...

0 Ruihang Miao, et al. ∙

research

∙ 11/29/2022

One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation

Neural Radiance Fields (NeRF) methods have proved effective as compact, ...

0 Shuangkang Fang, et al. ∙

research

∙ 07/12/2022

Synergistic Self-supervised and Quantization Learning

With the success of self-supervised learning (SSL), it has become a main...

0 Yun-Hao Cao, et al. ∙

research

∙ 07/12/2022

Collaborative Neural Rendering using Anime Character Sheets

Drawing images of characters at desired poses is an essential but labori...

0 Zuzeng Lin, et al. ∙

research

∙ 06/26/2022

Perceptual Conversational Head Generation with Regularized Driver and Enhanced Renderer

This paper reports our solution for MultiMedia ViCo 2022 Conversational ...

0 Ailin Huang, et al. ∙

research

∙ 03/04/2022

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

Combinatorial optimization is a well-established area in operations rese...

12 Maxime Gasse, et al. ∙

research

∙ 01/25/2022

ML4CO-KIDA: Knowledge Inheritance in Data Aggregation

The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021...

0 Zixuan Cao, et al. ∙

research

∙ 11/27/2021

FQ-ViT: Fully Quantized Vision Transformer without Retraining

Network quantization significantly reduces model inference complexity an...

0 Yang Lin, et al. ∙

research

∙ 11/01/2021

Arch-Net: Model Distillation for Architecture Agnostic Model Deployment

Vast requirement of computation power of Deep Neural Networks is a major...

0 Weixin Xu, et al. ∙

research

∙ 11/12/2020

RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for ...

8 Zhewei Huang, et al. ∙

research

∙ 08/30/2020

Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond

Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers f...

0 Dachao Lin, et al. ∙

research

∙ 02/28/2020

Detecting and Recovering Adversarial Examples: An Input Sensitivity Guided Method

Deep neural networks undergo rapid development and achieve notable succe...

0 Mingxuan Li, et al. ∙

research

∙ 03/11/2019

Stroke-based Artistic Rendering Agent with Deep Reinforcement Learning

Excellent painters can use only a few strokes to create a fantastic pain...

18 Zhewei Huang, et al. ∙

research

∙ 03/11/2019

Learning to Paint with Model-based Deep Reinforcement Learning

We show how to teach machines to paint like human painters, who can use ...

0 Zhewei Huang, et al. ∙

research

∙ 07/18/2018

Harmonic Adversarial Attack Method

Adversarial attacks find perturbations that can fool models into misclas...

0 Wen Heng, et al. ∙

research

∙ 06/23/2018

Stroke-based Character Recognition with Deep Reinforcement Learning

The stroke sequence of characters is significant for the character recog...

0 Zhewei Huang, et al. ∙

research

∙ 05/17/2018

Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks

In this paper we explore acceleration techniques for large scale nonconv...

0 Guangzeng Xie, et al. ∙

research

∙ 04/02/2018

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

In the NIPS 2017 Learning to Run challenge, participants were tasked wit...

0 Łukasz Kidziński, et al. ∙

research

∙ 12/25/2017

Learning to Run with Actor-Critic Ensemble

We introduce an Actor-Critic Ensemble(ACE) method for improving the perf...

0 Zhewei Huang, et al. ∙

research

∙ 06/22/2017

Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks

Quantized Neural Networks (QNNs), which use low bitwidth numbers for rep...

0 Shuchang Zhou, et al. ∙

research

∙ 05/14/2017

GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data

Object Transfiguration replaces an object in an image with another objec...

0 Shuchang Zhou, et al. ∙

research

∙ 04/11/2017

EAST: An Efficient and Accurate Scene Text Detector

Previous approaches for scene text detection have already achieved promi...

0 Xinyu Zhou, et al. ∙

research

∙ 12/01/2016

Training Bit Fully Convolutional Network for Fast Semantic Segmentation

Fully convolutional neural networks give accurate, per-pixel prediction ...

0 He Wen, et al. ∙

research

∙ 11/30/2016

Effective Quantization Methods for Recurrent Neural Networks

Reducing bit-widths of weights, activations, and gradients of a Neural N...

0 Qinyao He, et al. ∙

research

∙ 06/29/2016

Scene Text Detection via Holistic, Multi-Channel Prediction

Recently, scene text detection has become an active research topic in co...

0 Cong Yao, et al. ∙

research

∙ 06/20/2016

DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

We propose DoReFa-Net, a method to train convolutional neural networks t...

0 Shuchang Zhou, et al. ∙

research

∙ 12/31/2015

Exploiting Local Structures with the Kronecker Layer in Convolutional Networks

In this paper, we propose and study a technique to reduce the number of ...

0 Shuchang Zhou, et al. ∙

research

∙ 11/30/2015

Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4

Different from focused texts present in natural images, which are captur...

0 Cong Yao, et al. ∙

research

∙ 07/30/2015

Multilinear Map Layer: Prediction Regularization by Structural Constraint

In this paper we propose and study a technique to impose structural cons...

0 Shuchang Zhou, et al. ∙

research

∙ 07/21/2015

Compression of Fully-Connected Layer in Neural Network by Kronecker Product

In this paper we propose and study a technique to reduce the number of p...

0 Shuchang Zhou, et al. ∙

research

∙ 06/10/2015

ICDAR 2015 Text Reading in the Wild Competition

Recently, text detection and recognition in natural scenes are becoming ...

0 Xinyu Zhou, et al. ∙

research

∙ 10/03/2014

Group Orbit Optimization: A Unified Approach to Data Normalization

In this paper we propose and study an optimization problem over a matrix...

0 Shuchang Zhou, et al. ∙

research

∙ 07/22/2013

Kinetic Energy Plus Penalty Functions for Sparse Estimation

In this paper we propose and study a family of sparsity-inducing penalty...

0 Zhihua Zhang, et al. ∙

Shuchang Zhou

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