Search Results for author: Yue Cao

Found 48 papers, 30 papers with code

Revisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot

no code implementations EMNLP 2021 Yitao Cai, Yue Cao, Xiaojun Wan

Concretely, we transform a sentence into a variety of different semantic or syntactic representations (including AMR, UD, and latent semantic representation), and then decode the sentence back from the semantic representations.

Paraphrase Generation

Self-supervised Learning from 100 Million Medical Images

no code implementations4 Jan 2022 Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Dominik Neumann, Pragneshkumar Patel, R. S. Vishwanath, James M. Balter, Yue Cao, Sasa Grbic, Dorin Comaniciu

Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the creation of large and curated sets of annotated training examples.

Computed Tomography (CT) Contrastive Learning +1

A Simple Baseline for Zero-shot Semantic Segmentation with Pre-trained Vision-language Model

1 code implementation29 Dec 2021 Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Han Hu, Xiang Bai

Recently, zero-shot image classification by vision-language pre-training has demonstrated incredible achievements, that the model can classify arbitrary category without seeing additional annotated images of that category.

Image Classification Language Modelling +3

SimMIM: A Simple Framework for Masked Image Modeling

2 code implementations18 Nov 2021 Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, Han Hu

We also leverage this approach to facilitate the training of a 3B model (SwinV2-G), that by $40\times$ less data than that in previous practice, we achieve the state-of-the-art on four representative vision benchmarks.

Representation Learning Self-Supervised Image Classification

Swin Transformer V2: Scaling Up Capacity and Resolution

5 code implementations18 Nov 2021 Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo

Our techniques are generally applicable for scaling up vision models, which has not been widely explored as that of NLP language models, partly due to the following difficulties in training and applications: 1) vision models often face instability issues at scale and 2) many downstream vision tasks require high resolution images or windows and it is not clear how to effectively transfer models pre-trained at low resolutions to higher resolution ones.

 Ranked #1 on Object Detection on COCO test-dev (using extra training data)

Action Classification Image Classification +3

Bootstrap Your Object Detector via Mixed Training

1 code implementation NeurIPS 2021 Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Stephen Lin, Han Hu, Xiang Bai

We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free.

Data Augmentation Object Detection

Learning-Based Path Planning for Long-Range Autonomous Valet Parking

no code implementations23 Sep 2021 Muhammad Khalid, Liang Wang, Kezhi Wang, Cunhua Pan, Nauman Aslam, Yue Cao

In this paper, to reduce the congestion rate at the city center and increase the quality of experience (QoE) of each user, the framework of long-range autonomous valet parking (LAVP) is presented, where an Electric Autonomous Vehicle (EAV) is deployed in the city, which can pick up, drop off users at their required spots, and then drive to the car park out of city center autonomously.

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design

1 code implementation24 Jun 2021 Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang shen

Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.

Video Swin Transformer

9 code implementations24 Jun 2021 Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, Han Hu

The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks.

Ranked #8 on Action Classification on Kinetics-600 (using extra training data)

Action Classification Action Recognition +3

Continual Learning for Neural Machine Translation

no code implementations NAACL 2021 Yue Cao, Hao-Ran Wei, Boxing Chen, Xiaojun Wan

In practical applications, NMT models are usually trained on a general domain corpus and then fine-tuned by continuing training on the in-domain corpus.

Continual Learning Knowledge Distillation +2

Prediction of Prognosis and Survival of Patients with Gastric Cancer by Weighted Improved Random Forest Model

no code implementations Archives of Medical Science 2021 Cheng Xu, Jing Wang, TianLong Zheng, Yue Cao, Fan Ye

Among the 10 public datasets, the Random Forest weighted in accuracy has the best performance on 6 datasets, with an average increase of 1. 44% in accuracy and an average increase of 1. 2% in AUC.

Epidemiology

Group-Free 3D Object Detection via Transformers

2 code implementations ICCV 2021 Ze Liu, Zheng Zhang, Yue Cao, Han Hu, Xin Tong

Instead of grouping local points to each object candidate, our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers \cite{vaswani2017attention}, where the contribution of each point is automatically learned in the network training.

3D Object Detection

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

43 code implementations ICCV 2021 Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.

Ranked #3 on Semantic Segmentation on FoodSeg103 (using extra training data)

Image Classification Instance Segmentation +2

Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser

1 code implementation18 Mar 2021 Yue Cao, Xiaohe Wu, Shuran Qi, Xiao Liu, Zhongqin Wu, WangMeng Zuo

To begin with, the pre-trained denoiser is used to generate the pseudo clean images for the test images.

Denoising

ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation

1 code implementation EACL 2021 Qingxiu Dong, Xiaojun Wan, Yue Cao

We propose ParaSCI, the first large-scale paraphrase dataset in the scientific field, including 33, 981 paraphrase pairs from ACL (ParaSCI-ACL) and 316, 063 pairs from arXiv (ParaSCI-arXiv).

Paraphrase Generation

Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty

no code implementations1 Jan 2021 Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.

Image Classification Variational Inference

Global Context Networks

3 code implementations24 Dec 2020 Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu

The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies within an image, via aggregating query-specific global context to each query position.

Instance Segmentation Object Detection

DR 21 South Filament: a Parsec-sized Dense Gas Accretion Flow onto the DR 21 Massive Young Cluster

no code implementations4 Dec 2020 Bo Hu, Keping Qiu, Yue Cao, Junhao Liu, Yuwei Wang, Guangxing Li, Zhiqiang Shen, Juan Li, Junzhi Wang, Bin Li, Jian Dong

DR21 south filament (DR21SF) is a unique component of the giant network of filamentary molecular clouds in the north region of Cygnus X complex.

Astrophysics of Galaxies

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning

5 code implementations CVPR 2021 Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, Han Hu

We argue that the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be sub-optimal for downstream tasks requiring dense pixel predictions.

Contrastive Learning Object Detection +2

Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising

2 code implementations23 Oct 2020 Yali Peng, Yue Cao, Shigang Liu, Jian Yang, WangMeng Zuo

To cope with this issue, this paper presents a multi-level wavelet residual network (MWRN) architecture as well as a progressive training (PTMWRN) scheme to improve image denoising performance.

Image Denoising

Unpaired Learning of Deep Image Denoising

1 code implementation ECCV 2020 Xiaohe Wu, Ming Liu, Yue Cao, Dongwei Ren, WangMeng Zuo

As for knowledge distillation, we first apply the learned noise models to clean images to synthesize a paired set of training images, and use the real noisy images and the corresponding denoising results in the first stage to form another paired set.

Image Denoising Knowledge Distillation +1

RepPoints V2: Verification Meets Regression for Object Detection

1 code implementation NeurIPS 2020 Yihong Chen, Zheng Zhang, Yue Cao, Li-Wei Wang, Stephen Lin, Han Hu

Though RepPoints provides high performance, we find that its heavy reliance on regression for object localization leaves room for improvement.

Instance Segmentation Object Detection +2

Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment

no code implementations8 Jul 2020 Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Eli Gibson, R. S. Vishwanath, Abishek Balachandran, James M. Balter, Yue Cao, Ramandeep Singh, Subba R. Digumarthy, Mannudeep K. Kalra, Sasa Grbic, Dorin Comaniciu

In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e. g., by 8% to 0. 91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs.

General Classification

A Closer Look at Local Aggregation Operators in Point Cloud Analysis

1 code implementation ECCV 2020 Ze Liu, Han Hu, Yue Cao, Zheng Zhang, Xin Tong

Our investigation reveals that despite the different designs of these operators, all of these operators make surprisingly similar contributions to the network performance under the same network input and feature numbers and result in the state-of-the-art accuracy on standard benchmarks.

3D Semantic Segmentation

Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization

no code implementations ACL 2020 Yue Cao, Hui Liu, Xiaojun Wan

However, it is a big challenge for the model to directly learn cross-lingual summarization as it requires learning to understand different languages and learning how to summarize at the same time.

Cross-Lingual Transfer

Disentangled Non-Local Neural Networks

4 code implementations ECCV 2020 Minghao Yin, Zhuliang Yao, Yue Cao, Xiu Li, Zheng Zhang, Stephen Lin, Han Hu

This paper first studies the non-local block in depth, where we find that its attention computation can be split into two terms, a whitened pairwise term accounting for the relationship between two pixels and a unary term representing the saliency of every pixel.

Action Recognition Object Detection +1

Memory Enhanced Global-Local Aggregation for Video Object Detection

2 code implementations CVPR 2020 Yihong Chen, Yue Cao, Han Hu, Li-Wei Wang

We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information.

Video Object Detection

Cross-Iteration Batch Normalization

2 code implementations CVPR 2021 Zhuliang Yao, Yue Cao, Shuxin Zheng, Gao Huang, Stephen Lin

We thus compensate for the network weight changes via a proposed technique based on Taylor polynomials, so that the statistics can be accurately estimated and batch normalization can be effectively applied.

Image Classification Object Detection

Energy-based Graph Convolutional Networks for Scoring Protein Docking Models

no code implementations28 Dec 2019 Yue Cao, Yang shen

Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking.

Learning to Optimize in Swarms

1 code implementation NeurIPS 2019 Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks.

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

9 code implementations25 Apr 2019 Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu

In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation.

Instance Segmentation Object Detection +1

Spatial-Temporal Relation Networks for Multi-Object Tracking

no code implementations ICCV 2019 Jiarui Xu, Yue Cao, Zheng Zhang, Han Hu

Recent progress in multiple object tracking (MOT) has shown that a robust similarity score is key to the success of trackers.

Multi-Object Tracking Multiple Object Tracking

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

1 code implementation9 Mar 2019 Weizhi Ma, Min Zhang, Yue Cao, Woojeong, Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, Xiang Ren

The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.

Knowledge Graphs Recommendation Systems

Deep Triplet Quantization

1 code implementation1 Feb 2019 Bin Liu, Yue Cao, Mingsheng Long, Jian-Min Wang, Jingdong Wang

We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets.

Image Retrieval Quantization

Bayesian active learning for optimization and uncertainty quantification in protein docking

1 code implementation31 Jan 2019 Yue Cao, Yang shen

To the best of our knowledge, this study represents the first uncertainty quantification solution for protein docking, with theoretical rigor and comprehensive assessment.

Active Learning

Deep Cauchy Hashing for Hamming Space Retrieval

no code implementations CVPR 2018 Yue Cao, Mingsheng Long, Bin Liu, Jian-Min Wang

Due to its computation efficiency and retrieval quality, hashing has been widely applied to approximate nearest neighbor search for large-scale image retrieval, while deep hashing further improves the retrieval quality by end-to-end representation learning and hash coding.

Image Retrieval Representation Learning

HashGAN: Deep Learning to Hash With Pair Conditional Wasserstein GAN

no code implementations CVPR 2018 Yue Cao, Bin Liu, Mingsheng Long, Jian-Min Wang

The main idea is to augment the training data with nearly real images synthesized from a new Pair Conditional Wasserstein GAN (PC-WGAN) conditioned on the pairwise similarity information.

Image Retrieval Representation Learning

Deep Visual-Semantic Quantization for Efficient Image Retrieval

no code implementations CVPR 2017 Yue Cao, Mingsheng Long, Jian-Min Wang, Shichen Liu

This paper presents a compact coding solution with a focus on the deep learning to quantization approach, which improves retrieval quality by end-to-end representation learning and compact encoding and has already shown the superior performance over the hashing solutions for similarity retrieval.

Image Retrieval Quantization +1

Correlation Hashing Network for Efficient Cross-Modal Retrieval

no code implementations22 Feb 2016 Yue Cao, Mingsheng Long, Jian-Min Wang, Philip S. Yu

This paper presents a Correlation Hashing Network (CHN) approach to cross-modal hashing, which jointly learns good data representation tailored to hash coding and formally controls the quantization error.

Cross-Modal Retrieval Quantization

Learning Transferable Features with Deep Adaptation Networks

4 code implementations10 Feb 2015 Mingsheng Long, Yue Cao, Jian-Min Wang, Michael. I. Jordan

Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation.

Domain Adaptation Image Classification

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