Search Results for author: Ruimao Zhang

Found 35 papers, 16 papers with code

MetaDance: Few-shot Dancing Video Retargeting via Temporal-aware Meta-learning

no code implementations13 Jan 2022 Yuying Ge, Yibing Song, Ruimao Zhang, Ping Luo

Dancing video retargeting aims to synthesize a video that transfers the dance movements from a source video to a target person.

Meta-Learning

MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples

no code implementations6 Dec 2021 Yuying Ge, Ruimao Zhang, Ping Luo

This work proposes a novel framework named MetaCloth via meta-learning, which is able to learn unseen tasks of dense fashion landmark detection with only a few annotated samples.

Meta-Learning

End-to-End Dense Video Captioning with Parallel Decoding

1 code implementation ICCV 2021 Teng Wang, Ruimao Zhang, Zhichao Lu, Feng Zheng, Ran Cheng, Ping Luo

Dense video captioning aims to generate multiple associated captions with their temporal locations from the video.

Dense Video Captioning

Shallow Attention Network for Polyp Segmentation

1 code implementation2 Aug 2021 Jun Wei, Yiwen Hu, Ruimao Zhang, Zhen Li, S. Kevin Zhou, Shuguang Cui

To address the above issues, we propose the Shallow Attention Network (SANet) for polyp segmentation.

Video Polyp Segmentation

Crowd Counting via Perspective-Guided Fractional-Dilation Convolution

1 code implementation8 Jul 2021 Zhaoyi Yan, Ruimao Zhang, Hongzhi Zhang, Qingfu Zhang, WangMeng Zuo

One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect.

Crowd Counting

Multi-Compound Transformer for Accurate Biomedical Image Segmentation

1 code implementation28 Jun 2021 Yuanfeng Ji, Ruimao Zhang, Huijie Wang, Zhen Li, Lingyun Wu, Shaoting Zhang, Ping Luo

The recent vision transformer(i. e. for image classification) learns non-local attentive interaction of different patch tokens.

Image Classification Semantic correspondence +1

PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond

1 code implementation5 May 2021 Enze Xie, Wenhai Wang, Mingyu Ding, Ruimao Zhang, Ping Luo

Extensive experiments demonstrate the effectiveness of both PolarMask and PolarMask++, which achieve competitive results on instance segmentation in the challenging COCO dataset with single-model and single-scale training and testing, as well as new state-of-the-art results on rotate text detection and cell segmentation.

Ranked #47 on Instance Segmentation on COCO test-dev (using extra training data)

Cell Segmentation Instance Segmentation +2

PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery

1 code implementation30 Apr 2021 Weibing Zhao, Xu Yan, Jiantao Gao, Ruimao Zhang, Jiayan Zhang, Zhen Li, Song Wu, Shuguang Cui

In this paper, we address a fundamental problem in PCSR: How to downsample the dense point cloud with arbitrary scales while preserving the local topology of discarding points in a case-agnostic manner (i. e. without additional storage for point relationship)?

Parser-Free Virtual Try-on via Distilling Appearance Flows

1 code implementation CVPR 2021 Yuying Ge, Yibing Song, Ruimao Zhang, Chongjian Ge, Wei Liu, Ping Luo

A recent pioneering work employed knowledge distillation to reduce the dependency of human parsing, where the try-on images produced by a parser-based method are used as supervisions to train a "student" network without relying on segmentation, making the student mimic the try-on ability of the parser-based model.

Human Parsing Knowledge Distillation +1

Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion

1 code implementation7 Dec 2020 Xu Yan, Jiantao Gao, Jie Li, Ruimao Zhang, Zhen Li, Rui Huang, Shuguang Cui

In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input.

3D Semantic Scene Completion from a single RGB image Autonomous Driving +2

SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervision and Dynamic Self-Training

1 code implementation26 Nov 2020 Weijia Wu, Enze Xie, Ruimao Zhang, Wenhai Wang, Guan Pang, Zhen Li, Hong Zhou, Ping Luo

Although a polygon is a more accurate representation than an upright bounding box for text detection, the annotations of polygons are extremely expensive and challenging.

UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation

no code implementations16 Sep 2020 Yuanfeng Ji, Ruimao Zhang, Zhen Li, Jiamin Ren, Shaoting Zhang, Ping Luo

Unlike the recent neural architecture search (NAS) methods that typically searched the optimal operators in each network layer, but missed a good strategy to search for feature aggregations, this paper proposes a novel NAS method for 3D medical image segmentation, named UXNet, which searches both the scale-wise feature aggregation strategies as well as the block-wise operators in the encoder-decoder network.

Neural Architecture Search Semantic Segmentation +1

Exemplar Normalization for Learning Deep Representation

no code implementations CVPR 2020 Ruimao Zhang, Zhanglin Peng, Lingyun Wu, Zhen Li, Ping Luo

This work investigates a novel dynamic learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN), which is able to learn different normalization methods for different convolutional layers and image samples of a deep network.

Semantic Segmentation

Towards Photo-Realistic Virtual Try-On by Adaptively Generating$\leftrightarrow$Preserving Image Content

2 code implementations12 Mar 2020 Han Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu, WangMeng Zuo, Ping Luo

First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.

Semantic Segmentation Virtual Try-on

Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks

no code implementations ICCV 2019 Zhaoyang Zhang, Jingyu Li, Wenqi Shao, Zhanglin Peng, Ruimao Zhang, Xiaogang Wang, Ping Luo

ResNeXt, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers.

Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once

no code implementations ICCV 2019 Jiangfan Han, Xiaoyi Dong, Ruimao Zhang, Dong-Dong Chen, Weiming Zhang, Nenghai Yu, Ping Luo, Xiaogang Wang

Recently, generation-based methods have received much attention since they directly use feed-forward networks to generate the adversarial samples, which avoid the time-consuming iterative attacking procedure in optimization-based and gradient-based methods.

Classification General Classification

Switchable Normalization for Learning-to-Normalize Deep Representation

no code implementations22 Jul 2019 Ping Luo, Ruimao Zhang, Jiamin Ren, Zhanglin Peng, Jingyu Li

Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer?

SSN: Learning Sparse Switchable Normalization via SparsestMax

1 code implementation CVPR 2019 Wenqi Shao, Tianjian Meng, Jingyu Li, Ruimao Zhang, Yudian Li, Xiaogang Wang, Ping Luo

Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax.

Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?

no code implementations19 Nov 2018 Ping Luo, Zhanglin Peng, Jiamin Ren, Ruimao Zhang

Our results suggest that (1) using distinct normalizers improves both learning and generalization of a ConvNet; (2) the choices of normalizers are more related to depth and batch size, but less relevant to parameter initialization, learning rate decay, and solver; (3) different tasks and datasets have different behaviors when learning to select normalizers.

Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview

no code implementations10 Oct 2018 Lili Huang, Jiefeng Peng, Ruimao Zhang, Guanbin Li, Liang Lin

Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the field of computer vision.

Representation Learning Semantic Segmentation

Attentive Crowd Flow Machines

no code implementations1 Sep 2018 Lingbo Liu, Ruimao Zhang, Jiefeng Peng, Guanbin Li, Bowen Du, Liang Lin

Traffic flow prediction is crucial for urban traffic management and public safety.

SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification

no code implementations16 Jul 2018 Ruimao Zhang, Hongbin Sun, Jingyu Li, Yuying Ge, Liang Lin, Ping Luo, Xiaogang Wang

To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN).

Video-Based Person Re-Identification

Differentiable Learning-to-Normalize via Switchable Normalization

3 code implementations ICLR 2019 Ping Luo, Jiamin Ren, Zhanglin Peng, Ruimao Zhang, Jingyu Li

We hope SN will help ease the usage and understand the normalization techniques in deep learning.

Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions

no code implementations27 Sep 2017 Ruimao Zhang, Liang Lin, Guangrun Wang, Meng Wang, WangMeng Zuo

Rather than relying on elaborative annotations (e. g., manually labeled semantic maps and relations), we train our deep model in a weakly-supervised learning manner by leveraging the descriptive sentences of the training images.

Scene Labeling Scene Understanding

Progressively Diffused Networks for Semantic Image Segmentation

no code implementations20 Feb 2017 Ruimao Zhang, Wei Yang, Zhanglin Peng, Xiaogang Wang, Liang Lin

This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemplar application.

Semantic Segmentation

Cost-Effective Active Learning for Deep Image Classification

1 code implementation13 Jan 2017 Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, Liang Lin

In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner.

Active Learning Classification +5

Deep Structured Scene Parsing by Learning with Image Descriptions

no code implementations CVPR 2016 Liang Lin, Guangrun Wang, Rui Zhang, Ruimao Zhang, Xiaodan Liang, WangMeng Zuo

This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i. e., a semantic object hierarchy with object interaction relations) that finely accords with human perception.

Scene Labeling Scene Understanding

Geometric Scene Parsing with Hierarchical LSTM

no code implementations7 Apr 2016 Zhanglin Peng, Ruimao Zhang, Xiaodan Liang, Xiaobai Liu, Liang Lin

This paper addresses the problem of geometric scene parsing, i. e. simultaneously labeling geometric surfaces (e. g. sky, ground and vertical plane) and determining the interaction relations (e. g. layering, supporting, siding and affinity) between main regions.

3D Reconstruction Scene Labeling

Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification

no code implementations19 Aug 2015 Ruimao Zhang, Liang Lin, Rui Zhang, WangMeng Zuo, Lei Zhang

Furthermore, each bit of our hashing codes is unequally weighted so that we can manipulate the code lengths by truncating the insignificant bits.

Image Retrieval Person Re-Identification

Deep Boosting: Layered Feature Mining for General Image Classification

no code implementations3 Feb 2015 Zhanglin Peng, Liang Lin, Ruimao Zhang, Jing Xu

Constructing effective representations is a critical but challenging problem in multimedia understanding.

Classification General Classification +1

Adaptive Scene Category Discovery with Generative Learning and Compositional Sampling

no code implementations2 Feb 2015 Liang Lin, Ruimao Zhang, Xiaohua Duan

During the iterations of inference, the model of each category is analytically updated by a generative learning algorithm.

Image Categorization

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