Tianyu Li

Georgia Institute of Technology

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Xinxiao Wu

Beijing Institute of Technology

Sehoon Ha

Georgia Institute of Technology

Guoqing Wang

University of Electronic Science and Technology of China

Heng Tao Shen

University of Electronic Science and Technology of China

Krasimir Vasilev

Flinders University

Zhongping Qu

Xi'an Jiaotong University

Zhaobing Qiu

Fuzhou University

Tianqi Zhang

University of Sydney

Zongyong Deng

Chengdu University

Chaofan Qiao

University of Electronic Science and Technology of China

Cooperative Institutions

Chinese Academy of Sciences

Xi'an Jiaotong University

University of Chinese Academy of Sciences

University of Electronic Science and Technology of China

Sichuan University

Zhejiang University

Shanghai Jiao Tong University

National University of Defense Technology

Wuhan University

Harbin Institute of Technology

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Variational Deep Representation Learning for Cross-Modal Retrieval

Lecture notes in computer science (2021)

Chen Yang Zongyong Deng Tianyu Li Hao Liu Libo Liu

Autoencoder

Representation

Hinge loss

Similarity (geometry)

Semantic space

10.1007/978-3-030-88007-1_41

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Citations (1)

Gibberellin and shikimic acid enhance ascorbic acid accumulation and ROS scavenging ability to delay the senescence of postharvest jujube fruit

Postharvest Biology and Technology (2024)

K. Jiang Xiaohuan Zhang Tianyu Li Jiaqi Liu Mengjun Liu

Shikimic acid

Senescence

10.1016/j.postharvbio.2024.113340

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Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis

arXiv (Cornell University) (2017)

Rui Huang Shu Zhang Tianyu Li Ran He

Photorealistic frontal view synthesis from a single face image has a wide range of applications in the field of face recognition. Although data-driven deep learning methods have been proposed to address this problem by seeking solutions from ample face data, this problem is still challenging because it is intrinsically ill-posed. This paper proposes a Two-Pathway Generative Adversarial Network (TP-GAN) for photorealistic frontal view synthesis by simultaneously perceiving global structures and local details. Four landmark located patch networks are proposed to attend to local textures in addition to the commonly used global encoder-decoder network. Except for the novel architecture, we make this ill-posed problem well constrained by introducing a combination of adversarial loss, symmetry loss and identity preserving loss. The combined loss function leverages both frontal face distribution and pre-trained discriminative deep face models to guide an identity preserving inference of frontal views from profiles. Different from previous deep learning methods that mainly rely on intermediate features for recognition, our method directly leverages the synthesized identity preserving image for downstream tasks like face recognition and attribution estimation. Experimental results demonstrate that our method not only presents compelling perceptual results but also outperforms state-of-the-art results on large pose face recognition.

Discriminative model

10.48550/arxiv.1704.04086

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Citations (93)

ICPR 2024 Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results

Lecture notes in computer science (2024)

Boyang Li Xinyi Ying Ruojing Li Yongxian Liu Yangsi Shi

10.1007/978-3-031-80139-6_5

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Exploiting Human Pose for Weakly-Supervised Temporal Action Localization

Lecture notes in computer science (2019)

Bing Zhu Tianyu Li Xinxiao Wu

Action Recognition

10.1007/978-3-030-31726-3_40

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ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023)

Jeong-Hwan Kim Tianyu Li Sehoon Ha

Generating natural and physically feasible motions for legged robots has been a challenging problem due to its complex dynamics. In this work, we introduce a novel learning-based framework of autoregressive motion planner (ARMP) for quadruped locomotion and navigation. Our method can generate motion plans with an arbitrary length in an autore-gressive fashion, unlike most offline trajectory optimization algorithms for a fixed trajectory length. To this end, we first construct the motion library by solving a dense set of trajectory optimization problems for diverse scenarios and parameter settings. Then we learn the motion manifold from the dataset in a supervised learning fashion. We show that the proposed ARMP can generate physically plausible motions for various tasks and situations. We also showcase that our method can be successfully integrated with the recent robot navigation frameworks as a low-level controller and unleash the full capability of legged robots for complex indoor navigation.

Planner

Trajectory Optimization

10.1109/iros55552.2023.10341389

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Plasma power-to-X (PP2X): status and opportunities for non-thermal plasma technologies

Journal of Physics D Applied Physics (2024)

Jing Sun Zhongping Qu Yuting Gao Tianyu Li Jungmi Hong

Abstract This article discusses the ‘Power-to-X’ (P2X) concept, highlighting the integral role of non-thermal plasma (NTP) in P2X for the eco-friendly production of chemicals and valuable fuels. NTP with unique thermally non-equilibrium characteristics, enables exotic reactions to occur under ambient conditions. This review summarizes the plasma-based P2X systems, including plasma discharges, reactor configurations, catalytic or non-catalytic processes, and modelling techniques. Especially, the potential of NTP to directly convert stable molecules including CO2, CH4 and air/N2 is critically examined. Additionally, we further present and discuss hybrid technologies that integrate NTP with photocatalysis, electrocatalysis, and biocatalysis, broadening its applications in P2X. It concludes by identifying key challenges, such as high energy consumption, and calls for the outlook in plasma catalysis and complex reaction systems to generate valuable products efficiently and sustainably, and achieve the industrial viability of the proposed PP2X strategy.

Nonthermal plasma

10.1088/1361-6463/ad7bc4

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Weakly-Supervised Mirror Detection via Scribble Annotations

Proceedings of the AAAI Conference on Artificial Intelligence (2024)

Mingfeng Zha Yunqiang Pei Guoqing Wang Tianyu Li Yang Yang

Mirror detection is of great significance for avoiding false recognition of reflected objects in computer vision tasks. Existing mirror detection frameworks usually follow a supervised setting, which relies heavily on high quality labels and suffers from poor generalization. To resolve this, we instead propose the first weakly-supervised mirror detection framework and also provide the first scribble-based mirror dataset. Specifically, we relabel 10,158 images, most of which have a labeled pixel ratio of less than 0.01 and take only about 8 seconds to label. Considering that the mirror regions usually show great scale variation, and also irregular and occluded, thus leading to issues of incomplete or over detection, we propose a local-global feature enhancement (LGFE) module to fully capture the context and details. Moreover, it is difficult to obtain basic mirror structure using scribble annotation, and the distinction between foreground (mirror) and background (non-mirror) features is not emphasized caused by mirror reflections. Therefore, we propose a foreground-aware mask attention (FAMA), integrating mirror edges and semantic features to complete mirror regions and suppressing the influence of backgrounds. Finally, to improve the robustness of the network, we propose a prototype contrast loss (PCL) to learn more general foreground features across images. Extensive experiments show that our network outperforms relevant state-of-the-art weakly supervised methods, and even some fully supervised methods. The dataset and codes are available at https://github.com/winter-flow/WSMD.

10.1609/aaai.v38i7.28521

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AAMDM: Accelerated Auto-Regressive Motion Diffusion Model

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

Tianyu Li Calvin Qiao Guanqiao Ren KangKang Yin Sehoon Ha

10.1109/cvpr52733.2024.00178

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Virtual Blue Noise Lighting

Proceedings of the ACM on Computer Graphics and Interactive Techniques (2022)

Tianyu Li Wenyou Wang Daqi Lin Cem Yuksel

We introduce virtual blue noise lighting, a rendering pipeline for estimating indirect illumination with a blue noise distribution of virtual lights. Our pipeline is designed for virtual lights with non-uniform emission profiles that are more expensive to store, but required for properly and efficiently handling specular transport. Unlike the typical virtual light placement approaches that traverse light paths from the original light sources, we generate them starting from the camera. This avoids two important problems: wasted memory and computation with fully-occluded virtual lights, and excessive virtual light density around high-probability light paths. In addition, we introduce a parallel and adaptive sample elimination strategy to achieve a blue noise distribution of virtual lights with varying density. This addresses the third problem of virtual light placement by ensuring that they are not placed too close to each other, providing better coverage of the (indirectly) visible surfaces and further improving the quality of the final lighting estimation. For computing the virtual light emission profiles, we present a photon splitting technique that allows efficiently using a large number of photons, as it does not require storing them. During lighting estimation, our method allows using both global power-based and local BSDF important sampling techniques, combined via multiple importance sampling. In addition, we present an adaptive path extension method that avoids sampling nearby virtual lights for reducing the estimation error. We show that our method significantly outperforms path tracing and prior work in virtual lights in terms of both performance and image quality, producing a fast but biased estimate of global illumination.

Global illumination

Path tracing

10.1145/3543872

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Citations (1)