Search Results for author: Zhihao LI

Found 14 papers, 8 papers with code

Rendering Nighttime Image Via Cascaded Color and Brightness Compensation

1 code implementation19 Apr 2022 Zhihao LI, Si Yi, Zhan Ma

Image signal processing (ISP) is crucial for camera imaging, and neural networks (NN) solutions are extensively deployed for daytime scenes.

Tone Mapping

Event Transformer

no code implementations11 Apr 2022 Zhihao LI, M. Salman Asif, Zhan Ma

The event camera is a bio-vision inspired camera with high dynamic range, high response speed, and low power consumption, recently attracting extensive attention for its use in vast vision tasks.

Event-based vision

Hyperbolic Graph Neural Networks: A Review of Methods and Applications

1 code implementation28 Feb 2022 Menglin Yang, Min Zhou, Zhihao LI, Jiahong Liu, Lujia Pan, Hui Xiong, Irwin King

Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.

Graph Learning

Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

1 code implementation ICCV 2021 Zhihao Liang, Zhihao LI, Songcen Xu, Mingkui Tan, Kui Jia

State-of-the-art methods largely rely on a general pipeline that first learns point-wise features discriminative at semantic and instance levels, followed by a separate step of point grouping for proposing object instances.

3D Instance Segmentation Scene Understanding +1

DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency

1 code implementation ICCV 2021 Jiehong Lin, Zewei Wei, Zhihao LI, Songcen Xu, Kui Jia, Yuanqing Li

DualPoseNet stacks two parallel pose decoders on top of a shared pose encoder, where the implicit decoder predicts object poses with a working mechanism different from that of the explicit one; they thus impose complementary supervision on the training of pose encoder.

Pose Prediction

Quality-Aware Network for Human Parsing

1 code implementation10 Mar 2021 Lu Yang, Qing Song, Zhihui Wang, Zhiwei Liu, Songcen Xu, Zhihao LI

How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing.

Human Parsing Instance Segmentation +1

Illumination Estimation Challenge: experience of past two years

no code implementations31 Dec 2020 Egor Ershov, Alex Savchik, Ilya Semenkov, Nikola Banić, Karlo Koscević, Marko Subašić, Alexander Belokopytov, Zhihao LI, Arseniy Terekhin, Daria Senshina, Artem Nikonorov, Yanlin Qian, Marco Buzzelli, Riccardo Riva, Simone Bianco, Raimondo Schettini, Sven Lončarić, Dmitry Nikolaev

The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased.

Color Constancy

An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset

2 code implementations14 Feb 2020 Zhicheng Gu, Zhihao LI, Xuan Di, Rongye Shi

The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking.

Autonomous Driving Self-Driving Cars

RETHINKING SELF-DRIVING : MULTI -TASK KNOWLEDGE FOR BETTER GENERALIZATION AND ACCIDENT EXPLANATION ABILITY

no code implementations ICLR 2019 Zhihao LI, Toshiyuki MOTOYOSHI, Kazuma Sasaki, Tetsuya OGATA, Shigeki SUGANO

Current end-to-end deep learning driving models have two problems: (1) Poor generalization ability of unobserved driving environment when diversity of train- ing driving dataset is limited (2) Lack of accident explanation ability when driving models don’t work as expected.

Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability

1 code implementation28 Sep 2018 Zhihao Li, Toshiyuki Motoyoshi, Kazuma Sasaki, Tetsuya OGATA, Shigeki SUGANO

Current end-to-end deep learning driving models have two problems: (1) Poor generalization ability of unobserved driving environment when diversity of training driving dataset is limited (2) Lack of accident explanation ability when driving models don't work as expected.

Geometry-Contrastive GAN for Facial Expression Transfer

1 code implementation6 Feb 2018 Fengchun Qiao, Naiming Yao, Zirui Jiao, Zhihao LI, Hui Chen, Hongan Wang

Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions.

Contrastive Learning

Transfer of View-manifold Learning to Similarity Perception of Novel Objects

no code implementations31 Mar 2017 Xingyu Lin, Hao Wang, Zhihao LI, Yimeng Zhang, Alan Yuille, Tai Sing Lee

We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience.

Metric Learning

Searching Action Proposals via Spatial Actionness Estimation and Temporal Path Inference and Tracking

no code implementations23 Aug 2016 Nannan Li, Dan Xu, Zhenqiang Ying, Zhihao LI, Ge Li

In this paper, we address the problem of searching action proposals in unconstrained video clips.

Frame

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