Search Results for author: Michael Ying Yang

Found 62 papers, 25 papers with code

BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models

no code implementations31 Aug 2023 Yao Wei, George Vosselman, Michael Ying Yang

3D building generation with low data acquisition costs, such as single image-to-3D, becomes increasingly important.

Denoising Image to 3D

Interactive Image Segmentation with Cross-Modality Vision Transformers

1 code implementation5 Jul 2023 Kun Li, George Vosselman, Michael Ying Yang

Interactive image segmentation aims to segment the target from the background with the manual guidance, which takes as input multimodal data such as images, clicks, scribbles, and bounding boxes.

Image Segmentation Interactive Segmentation +1

Learning Similarity between Scene Graphs and Images with Transformers

no code implementations2 Apr 2023 Yuren Cong, Wentong Liao, Bodo Rosenhahn, Michael Ying Yang

Scene graph generation is conventionally evaluated by (mean) Recall@K, which measures the ratio of correctly predicted triplets that appear in the ground truth.

Contrastive Learning Graph Generation +3

LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints

1 code implementation27 Feb 2023 Mengmeng Liu, Hao Cheng, Lin Chen, Hellward Broszio, Jiangtao Li, Runjiang Zhao, Monika Sester, Michael Ying Yang

Trajectory prediction for autonomous driving must continuously reason the motion stochasticity of road agents and comply with scene constraints.

Autonomous Driving Trajectory Prediction

Generating Evidential BEV Maps in Continuous Driving Space

1 code implementation6 Feb 2023 Yunshuang Yuan, Hao Cheng, Michael Ying Yang, Monika Sester

Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown.

Autonomous Driving object-detection +1

Attribute-Centric Compositional Text-to-Image Generation

no code implementations4 Jan 2023 Yuren Cong, Martin Renqiang Min, Li Erran Li, Bodo Rosenhahn, Michael Ying Yang

We further propose an attribute-centric contrastive loss to avoid overfitting to overrepresented attribute compositions.


SSGVS: Semantic Scene Graph-to-Video Synthesis

no code implementations11 Nov 2022 Yuren Cong, Jinhui Yi, Bodo Rosenhahn, Michael Ying Yang

A semantic scene graph-to-video synthesis framework (SSGVS), based on the pre-trained VSG encoder, VQ-VAE, and auto-regressive Transformer, is proposed to synthesize a video given an initial scene image and a non-fixed number of semantic scene graphs.

Image Generation

Flow-based GAN for 3D Point Cloud Generation from a Single Image

1 code implementation8 Oct 2022 Yao Wei, George Vosselman, Michael Ying Yang

Generating a 3D point cloud from a single 2D image is of great importance for 3D scene understanding applications.

Point Cloud Generation Scene Understanding

GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model

1 code implementation16 Sep 2022 Hao Cheng, Mengmeng Liu, Lin Chen, Hellward Broszio, Monika Sester, Michael Ying Yang

This paper proposes an attention-based graph model, named GATraj, which achieves a good balance of prediction accuracy and inference speed.

Autonomous Driving Robot Navigation +1

RelTR: Relation Transformer for Scene Graph Generation

1 code implementation27 Jan 2022 Yuren Cong, Michael Ying Yang, Bodo Rosenhahn

Different objects in the same scene are more or less related to each other, but only a limited number of these relationships are noteworthy.

Graph Generation object-detection +2

Disentangled Lifespan Face Synthesis

no code implementations ICCV 2021 Sen He, Wentong Liao, Michael Ying Yang, Yi-Zhe Song, Bodo Rosenhahn, Tao Xiang

The generated face image given a target age code is expected to be age-sensitive reflected by bio-plausible transformations of shape and texture, while being identity preserving.

Face Generation

Spatial-Temporal Transformer for Dynamic Scene Graph Generation

1 code implementation ICCV 2021 Yuren Cong, Wentong Liao, Hanno Ackermann, Bodo Rosenhahn, Michael Ying Yang

Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation.

Scene Graph Generation Video Understanding +1

Text to Image Generation with Semantic-Spatial Aware GAN

1 code implementation CVPR 2022 Kai Hu, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn

Text-to-image synthesis (T2I) aims to generate photo-realistic images which are semantically consistent with the text descriptions.

Sentence Embedding Sentence-Embedding

Context-Aware Layout to Image Generation with Enhanced Object Appearance

1 code implementation CVPR 2021 Sen He, Wentong Liao, Michael Ying Yang, Yongxin Yang, Yi-Zhe Song, Bodo Rosenhahn, Tao Xiang

We argue that these are caused by the lack of context-aware object and stuff feature encoding in their generators, and location-sensitive appearance representation in their discriminators.

Layout-to-Image Generation

Bidirectional Multi-scale Attention Networks for Semantic Segmentation of Oblique UAV Imagery

1 code implementation5 Feb 2021 Ye Lyu, George Vosselman, Gui-Song Xia, Michael Ying Yang

Semantic segmentation for aerial platforms has been one of the fundamental scene understanding task for the earth observation.

Scene Understanding Semantic Segmentation

Self-supervised monocular depth estimation from oblique UAV videos

1 code implementation19 Dec 2020 Logambal Madhuanand, Francesco Nex, Michael Ying Yang

Monocular video frames are used for training the deep learning model which learns depth and pose information jointly through two different networks, one each for depth and pose.

3D Reconstruction Image Generation +4

LGENet: Local and Global Encoder Network for Semantic Segmentation of Airborne Laser Scanning Point Clouds

no code implementations18 Dec 2020 Yaping Lin, George Vosselman, Yanpeng Cao, Michael Ying Yang

Interpretation of Airborne Laser Scanning (ALS) point clouds is a critical procedure for producing various geo-information products like 3D city models, digital terrain models and land use maps.

Semantic Segmentation

Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors

no code implementations7 Dec 2020 Fan Wang, Jiangxin Yang, Yanlong Cao, Yanpeng Cao, Michael Ying Yang

Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution optical sensors, facilitating better-performing target detection and autonomous navigation in a wide range of robotics applications.

3D Reconstruction Autonomous Navigation +1

Real-time Semantic Segmentation with Context Aggregation Network

no code implementations2 Nov 2020 Michael Ying Yang, Saumya Kumaar, Ye Lyu, Francesco Nex

With the increasing demand of autonomous systems, pixelwise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for potential real-time applications.

Real-Time Semantic Segmentation Scene Understanding

Exploring Dynamic Context for Multi-path Trajectory Prediction

2 code implementations30 Oct 2020 Hao Cheng, Wentong Liao, Xuejiao Tang, Michael Ying Yang, Monika Sester, Bodo Rosenhahn

In our framework, first, the spatial context between agents is explored by using self-attention architectures.

Trajectory Forecasting

On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID

1 code implementation22 Jun 2020 Yang Long, Gui-Song Xia, Shengyang Li, Wen Yang, Michael Ying Yang, Xiao Xiang Zhu, Liangpei Zhang, Deren Li

After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation.

General Classification Image Classification +1

AMENet: Attentive Maps Encoder Network for Trajectory Prediction

1 code implementation15 Jun 2020 Hao Cheng, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn, Monika Sester

Trajectory prediction is critical for applications of planning safe future movements and remains challenging even for the next few seconds in urban mixed traffic.

Trajectory Prediction

LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery

no code implementations28 May 2020 Wentong Liao, Xiang Chen, Jingfeng Yang, Stefan Roth, Michael Goesele, Michael Ying Yang, Bodo Rosenhahn

This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation.

object-detection Object Detection +1

Plug & Play Convolutional Regression Tracker for Video Object Detection

2 code implementations2 Mar 2020 Ye Lyu, Michael Ying Yang, George Vosselman, Gui-Song Xia

As the tracker reuses the features from the detector, it is a very light-weighted increment to the detection network.

object-detection regression +1

MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic

1 code implementation14 Feb 2020 Hao Cheng, Wentong Liao, Michael Ying Yang, Monika Sester, Bodo Rosenhahn

In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future.

Autonomous Driving Intent Detection +1

NODIS: Neural Ordinary Differential Scene Understanding

1 code implementation ECCV 2020 Cong Yuren, Hanno Ackermann, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn

Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image.

Graph Generation Relationship Detection +2

Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features

no code implementations9 Dec 2019 Du Chen, Zewei He, Yanpeng Cao, Jiangxin Yang, Yanlong Cao, Michael Ying Yang, Siliang Tang, Yueting Zhuang

Firstly, we proposed a novel Orientation-Aware feature extraction and fusion Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i. e., 5 x 1, 1 x 5, and 3 x 3) for extracting orientation-aware features.

Image Super-Resolution

LIP: Learning Instance Propagation for Video Object Segmentation

no code implementations30 Sep 2019 Ye Lyu, George Vosselman, Gui-Song Xia, Michael Ying Yang

In recent years, the task of segmenting foreground objects from background in a video, i. e. video object segmentation (VOS), has received considerable attention.

Data Augmentation Instance Segmentation +3

Temporally Consistent Horizon Lines

1 code implementation23 Jul 2019 Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn

The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision.

3D Reconstruction Autonomous Vehicles +2

Unsupervised Domain Adaptation for Multispectral Pedestrian Detection

no code implementations7 Apr 2019 Dayan Guan, Xing Luo, Yanpeng Cao, Jiangxin Yang, Yanlong Cao, George Vosselman, Michael Ying Yang

In this paper, we propose a novel unsupervised domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain.

Autonomous Driving Pedestrian Detection +1

Target-Tailored Source-Transformation for Scene Graph Generation

no code implementations3 Apr 2019 Wentong Liao, Cuiling Lan, Wen-Jun Zeng, Michael Ying Yang, Bodo Rosenhahn

We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation.

graph construction Graph Generation +4

Robust object extraction from remote sensing data

no code implementations3 Apr 2019 Sophie Crommelinck, Mila Koeva, Michael Ying Yang, George Vosselman

The delineation approach to which the evaluation framework is applied, was previously introduced and is substantially improved in this study.

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

no code implementations14 Feb 2019 Yanpeng Cao, Dayan Guan, Yulun Wu, Jiangxin Yang, Yanlong Cao, Michael Ying Yang

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e. g. daytime and nighttime).

Autonomous Driving Pedestrian Detection

Security Event Recognition for Visual Surveillance

no code implementations26 Oct 2018 Michael Ying Yang, Wentong Liao, Chun Yang, Yanpeng Cao, Bodo Rosenhahn

The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.

UAVid: A Semantic Segmentation Dataset for UAV Imagery

2 code implementations24 Oct 2018 Ye Lyu, George Vosselman, Gui-Song Xia, Alper Yilmaz, Michael Ying Yang

There already exist several semantic segmentation datasets for comparison among semantic segmentation methods in complex urban scenes, such as the Cityscapes and CamVid datasets, where the side views of the objects are captured with a camera mounted on the driving car.

Autonomous Driving Object Recognition +3

Patch-based Evaluation of Dense Image Matching Quality

no code implementations25 Jul 2018 Zhenchao Zhang, Markus Gerke, George Vosselman, Michael Ying Yang

Due to the high cost of laser scanning, we want to explore the potential of using point clouds derived by dense image matching (DIM), as effective alternatives to laser scanning data.

Change Detection between Multimodal Remote Sensing Data Using Siamese CNN

1 code implementation25 Jul 2018 Zhenchao Zhang, George Vosselman, Markus Gerke, Devis Tuia, Michael Ying Yang

Detecting topographic changes in the urban environment has always been an important task for urban planning and monitoring.

Change Detection

Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

no code implementations27 Feb 2018 Dayan Guan, Yanpeng Cao, Jun Liang, Yanlong Cao, Michael Ying Yang

Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy.

Autonomous Driving Multi-Task Learning +2

Triplet-based Deep Similarity Learning for Person Re-Identification

1 code implementation9 Feb 2018 Wentong Liao, Michael Ying Yang, Ni Zhan, Bodo Rosenhahn

Moreover, we trained the model jointly on six different datasets, which differs from common practice - one model is just trained on one dataset and tested also on the same one.

Person Re-Identification

Temporally Object-based Video Co-Segmentation

no code implementations9 Feb 2018 Michael Ying Yang, Matthias Reso, Jun Tang, Wentong Liao, Bodo Rosenhahn

Therefore, we formulate a graphical model to select a proposal stream for each object in which the pairwise potentials consist of the appearance dissimilarity between different streams in the same video and also the similarity between the streams in different videos.

Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models

no code implementations9 Feb 2018 Michael Ying Yang, Wentong Liao, Yanpeng Cao, Bodo Rosenhahn

In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions.

Anomaly Detection General Classification

Unsupervised Deep Domain Adaptation for Pedestrian Detection

no code implementations9 Feb 2018 Lihang Liu, Weiyao Lin, Lisheng Wu, Yong Yu, Michael Ying Yang

This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes.

Pedestrian Detection Unsupervised Domain Adaptation

Slice Sampling Particle Belief Propagation

no code implementations9 Feb 2018 Oliver Mueller, Michael Ying Yang, Bodo Rosenhahn

We propose to avoid dependence on a proposal distribution by introducing a slice sampling based PBP algorithm.

Image Denoising

Vehicle Detection in Aerial Images

no code implementations22 Jan 2018 Michael Ying Yang, Wentong Liao, Xinbo Li, Bodo Rosenhahn

Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposed network and the final classifier.

object-detection Object Detection

Natural Language Guided Visual Relationship Detection

no code implementations16 Nov 2017 Wentong Liao, Lin Shuai, Bodo Rosenhahn, Michael Ying Yang

Most of the existing works treat this task as a pure visual classification task: each type of relationship or phrase is classified as a relation category based on the extracted visual features.

Relationship Detection Visual Relationship Detection

Object Recognition from very few Training Examples for Enhancing Bicycle Maps

no code implementations18 Sep 2017 Christoph Reinders, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn

These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples.

Object Recognition Transfer Learning

Towards Automated Cadastral Boundary Delineation from UAV Data

no code implementations6 Sep 2017 Sophie Crommelinck, Michael Ying Yang, Mila Koeva, Markus Gerke, Rohan Bennett, George Vosselman

This study proposes (i) a workflow that automatically extracts candidate cadastral boundaries from UAV orthoimages and (ii) a tool for their semi-automatic processing to delineate final cadastral boundaries.

Contour Detection Superpixels

Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation

no code implementations26 Feb 2017 Omid Hosseini Jafari, Oliver Groth, Alexander Kirillov, Michael Ying Yang, Carsten Rother

Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state of the state-of-the-art results for depth estimation and semantic labeling.

Depth Estimation Depth Prediction +1

Motion Segmentation via Global and Local Sparse Subspace Optimization

no code implementations24 Jan 2017 Michael Ying Yang, Hanno Ackermann, Weiyao Lin, Sitong Feng, Bodo Rosenhahn

In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model.

Clustering Motion Segmentation

Can Ground Truth Label Propagation from Video help Semantic Segmentation?

no code implementations3 Oct 2016 Siva Karthik Mustikovela, Michael Ying Yang, Carsten Rother

For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves extensive human effort.

Semantic Segmentation Video Segmentation +1

On Support Relations and Semantic Scene Graphs

no code implementations19 Sep 2016 Michael Ying Yang, Wentong Liao, Hanno Ackermann, Bodo Rosenhahn

In contrast to previous methods for extracting support relations, the proposed approach generates more accurate results, and does not require a pixel-wise semantic labeling of the scene.

Scene Understanding

Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image

no code implementations CVPR 2016 Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, Carsten Rother

In recent years, the task of estimating the 6D pose of object instances and complete scenes, i. e. camera localization, from a single input image has received considerable attention.

6D Pose Estimation 6D Pose Estimation using RGB +1

Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images

no code implementations ICCV 2015 Alexander Krull, Eric Brachmann, Frank Michel, Michael Ying Yang, Stefan Gumhold, Carsten Rother

This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares an observed and rendered image.

6D Pose Estimation 6D Pose Estimation using RGB

Automatic 3D Liver Segmentation Using Sparse Representation of Global and Local Image Information via Level Set Formulation

no code implementations6 Aug 2015 Saif Dawood Salman Al-Shaikhli, Michael Ying Yang, Bodo Rosenhahn

A sparse representation of both global (region-based) and local (voxel-wise) image information is embedded in a level set formulation to innovate a new cost function.

Liver Segmentation

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