Search Results for author: Mingyu Ding

Found 22 papers, 12 papers with code

CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer

no code implementations17 Jun 2022 Yao Mu, Shoufa Chen, Mingyu Ding, Jianyu Chen, Runjian Chen, Ping Luo

In visual control, learning transferable state representation that can transfer between different control tasks is important to reduce the training sample size.

Transfer Learning

ComPhy: Compositional Physical Reasoning of Objects and Events from Videos

no code implementations ICLR 2022 Zhenfang Chen, Kexin Yi, Yunzhu Li, Mingyu Ding, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan

In this paper, we take an initial step to highlight the importance of inferring the hidden physical properties not directly observable from visual appearances, by introducing the Compositional Physical Reasoning (ComPhy) dataset.

DaViT: Dual Attention Vision Transformers

2 code implementations7 Apr 2022 Mingyu Ding, Bin Xiao, Noel Codella, Ping Luo, Jingdong Wang, Lu Yuan

We show that these two self-attentions complement each other: (i) since each channel token contains an abstract representation of the entire image, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial locations, which in turn helps the global information modeling in channel attention.

Ranked #8 on Image Classification on ImageNet (using extra training data)

Image Classification Instance Segmentation +2

Context Autoencoder for Self-Supervised Representation Learning

3 code implementations7 Feb 2022 Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, Jingdong Wang

We introduce an alignment constraint, encouraging that the representations for masked patches, predicted from the encoded representations of visible patches, are aligned with the masked patch presentations computed from the encoder.

Instance Segmentation object-detection +5

Compressed Video Contrastive Learning

no code implementations NeurIPS 2021 Yuqi Huo, Mingyu Ding, Haoyu Lu, Nanyi Fei, Zhiwu Lu, Ji-Rong Wen, Ping Luo

To enhance the representation ability of the motion vectors, hence the effectiveness of our method, we design a cross guidance contrastive learning algorithm based on multi-instance InfoNCE loss, where motion vectors can take supervision signals from RGB frames and vice versa.

Contrastive Learning Representation Learning

Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language

no code implementations NeurIPS 2021 Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Joshua B. Tenenbaum, Chuang Gan

This is achieved by seamlessly integrating three components: a visual perception module, a concept learner, and a differentiable physics engine.

Visual Reasoning

L2M-GAN: Learning To Manipulate Latent Space Semantics for Facial Attribute Editing

2 code implementations CVPR 2021 Guoxing Yang, Nanyi Fei, Mingyu Ding, Guangzhen Liu, Zhiwu Lu, Tao Xiang

To overcome these limitations, we propose a novel latent space factorization model, called L2M-GAN, which is learned end-to-end and effective for editing both local and global attributes.


HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers

1 code implementation CVPR 2021 Mingyu Ding, Xiaochen Lian, Linjie Yang, Peng Wang, Xiaojie Jin, Zhiwu Lu, Ping Luo

Last, we proposed an efficient fine-grained search strategy to train HR-NAS, which effectively explores the search space, and finds optimal architectures given various tasks and computation resources.

Image Classification Neural Architecture Search +2

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

Cell Segmentation Instance Segmentation +3

Learning Versatile Neural Architectures by Propagating Network Codes

1 code implementation ICLR 2022 Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang, Ping Luo

(4) Thorough studies of NCP on inter-, cross-, and intra-tasks highlight the importance of cross-task neural architecture design, i. e., multitask neural architectures and architecture transferring between different tasks.

Neural Architecture Search Semantic Segmentation +1

IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning

1 code implementation ICLR 2021 Manli Zhang, Jianhong Zhang, Zhiwu Lu, Tao Xiang, Mingyu Ding, Songfang Huang

Importantly, at the episode-level, two SSL-FSL hybrid learning objectives are devised: (1) The consistency across the predictions of an FSL classifier from different extended episodes is maximized as an episode-level pretext task.

Few-Shot Learning Self-Supervised Learning +1

Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw

no code implementations1 Jan 2021 Yuqi Huo, Mingyu Ding, Haoyu Lu, Zhiwu Lu, Tao Xiang, Ji-Rong Wen, Ziyuan Huang, Jianwen Jiang, Shiwei Zhang, Mingqian Tang, Songfang Huang, Ping Luo

With the constrained jigsaw puzzles, instead of solving them directly, which could still be extremely hard, we carefully design four surrogate tasks that are more solvable but meanwhile still ensure that the learned representation is sensitive to spatiotemporal continuity at both the local and global levels.

Representation Learning

Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking

2 code implementations ECCV 2020 Jian-Feng Yan, Zizhuang Wei, Hongwei Yi, Mingyu Ding, Runze Zhang, Yisong Chen, Guoping Wang, Yu-Wing Tai

In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely $D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction.

Point cloud reconstruction

Segmenting Transparent Objects in the Wild

1 code implementation ECCV 2020 Enze Xie, Wenjia Wang, Wenhai Wang, Mingyu Ding, Chunhua Shen, Ping Luo

To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10, 428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets.

Semantic Segmentation Transparent objects

Domain-Adaptive Few-Shot Learning

1 code implementation19 Mar 2020 An Zhao, Mingyu Ding, Zhiwu Lu, Tao Xiang, Yulei Niu, Jiechao Guan, Ji-Rong Wen, Ping Luo

Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples.

Domain Adaptation Few-Shot Learning

Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation

1 code implementation ECCV 2020 Hongwei Yi, Zizhuang Wei, Mingyu Ding, Runze Zhang, Yisong Chen, Guoping Wang, Yu-Wing Tai

n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction.

3D Point Cloud Reconstruction Depth Estimation +1

Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow

no code implementations28 Nov 2019 Mingyu Ding, Zhe Wang, Bolei Zhou, Jianping Shi, Zhiwu Lu, Ping Luo

Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference.

Optical Flow Estimation Semantic Segmentation +2

CamNet: Coarse-to-Fine Retrieval for Camera Re-Localization

no code implementations ICCV 2019 Mingyu Ding, Zhe Wang, Jiankai Sun, Jianping Shi, Ping Luo

Camera re-localization is an important but challenging task in applications like robotics and autonomous driving.

Autonomous Driving

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