Search Results for author: Leida Li

Found 13 papers, 5 papers with code

StarVQA: Space-Time Attention for Video Quality Assessment

no code implementations22 Aug 2021 Fengchuang Xing, Yuan-Gen Wang, Hanpin Wang, Leida Li, Guopu Zhu

To capture the long-range spatiotemporal dependencies of a video sequence, StarVQA encodes the space-time position information of each patch to the input of the Transformer.

Video Quality Assessment Visual Question Answering

A Circular-Structured Representation for Visual Emotion Distribution Learning

no code implementations CVPR 2021 Jingyuan Yang, Jie Li, Leida Li, Xiumei Wang, Xinbo Gao

Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks.

Emotion Recognition

Generalizable No-Reference Image Quality Assessment via Deep Meta-learning

1 code implementation IEEE Transactions on Circuits and Systems for Video Technology 2021 Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi

Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions.

Meta-Learning No-Reference Image Quality Assessment

Searching Efficient Model-guided Deep Network for Image Denoising

no code implementations6 Apr 2021 Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, Leida Li, Guangming Shi

Similar to the success of NAS in high-level vision tasks, it is possible to find a memory and computationally efficient solution via NAS with highly competent denoising performance.

Image Denoising Neural Architecture Search

A new communication paradigm: from bit accuracy to semantic fidelity

no code implementations29 Jan 2021 Guangming Shi, Dahua Gao, Xiaodan Song, Jingxuan Chai, Minxi Yang, Xuemei Xie, Leida Li, Xuyang Li

In this article, we deploy semantics to solve the spectrum and power bottleneck and propose a first understanding and then transmission framework with high semantic fidelity.

Networking and Internet Architecture

Cuid: A new study of perceived image quality and its subjective assessment

no code implementations28 Sep 2020 Lucie Lévêque, Ji Yang, Xiaohan Yang, Pengfei Guo, Kenneth Dasalla, Leida Li, Yingying Wu, Hantao Liu

It is thus critical to acquire reliable subjective data with controlled perception experiments that faithfully reflect human behavioural responses to distortions in visual signals.

Image Quality Assessment

MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment

1 code implementation CVPR 2020 Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi

The underlying idea is to learn the meta-knowledge shared by human when evaluating the quality of images with various distortions, which can then be adapted to unknown distortions easily.

Meta-Learning No-Reference Image Quality Assessment

PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression

1 code implementation11 Sep 2019 Sicheng Zhao, Zizhou Jia, Hui Chen, Leida Li, Guiguang Ding, Kurt Keutzer

By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the emotion regression performance.

Deep Attention Emotion Classification +1

Incremental Few-Shot Learning for Pedestrian Attribute Recognition

no code implementations2 Jun 2019 Liuyu Xiang, Xiaoming Jin, Guiguang Ding, Jungong Han, Leida Li

Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications.

Few-Shot Learning Pedestrian Attribute Recognition

Semantic Adversarial Network for Zero-Shot Sketch-Based Image Retrieval

no code implementations7 May 2019 Xinxun Xu, Hao Wang, Leida Li, Cheng Deng

Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario.

Cross-Modal Retrieval Sketch-Based Image Retrieval

Image Quality Assessment Guided Deep Neural Networks Training

3 code implementations13 Aug 2017 Zhuo Chen, Weisi Lin, Shiqi Wang, Long Xu, Leida Li

For many computer vision problems, the deep neural networks are trained and validated based on the assumption that the input images are pristine (i. e., artifact-free).

Data Augmentation Image Classification +1

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