Search Results for author: Shan Li

Found 8 papers, 2 papers with code

LSAP: Rethinking Inversion Fidelity, Perception and Editability in GAN Latent Space

no code implementations26 Sep 2022 Cao Pu, Lu Yang, Dongxv Liu, Zhiwei Liu, Wenguan Wang, Shan Li, Qing Song

In this work, we first point out that these two characteristics are related to the degree of alignment (or disalignment) of the inverse codes with the synthetic distribution.

Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks

no code implementations15 Jun 2022 Clemens JS Schaefer, Siddharth Joshi, Shan Li, Raul Blazquez

Quantizing the parameters and operations to lower bit-precision offers substantial memory and energy savings for neural network inference, facilitating the use of DNNs on edge computing platforms.

Edge-computing Quantization

RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations

no code implementations12 Aug 2020 Wenjing Yan, Shan Li, Chengtao Que, JiQuan Pei, Weihong Deng

Much of the work on automatic facial expression recognition relies on databases containing a certain number of emotion classes and their exaggerated facial configurations (generally six prototypical facial expressions), based on Ekman's Basic Emotion Theory.

Facial Expression Recognition Multi-Label Learning

Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

3 code implementations15 Jun 2020 Claudionor N. Coelho Jr., Aki Kuusela, Shan Li, Hao Zhuang, Thea Aarrestad, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Adrian Alan Pol, Sioni Summers

Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore reduction in model size, latency and energy consumption.

Quantization

A Deeper Look at Facial Expression Dataset Bias

no code implementations25 Apr 2019 Shan Li, Weihong Deng

Datasets play an important role in the progress of facial expression recognition algorithms, but they may suffer from obvious biases caused by different cultures and collection conditions.

Facial Expression Recognition

Deep Facial Expression Recognition: A Survey

7 code implementations23 Apr 2018 Shan Li, Weihong Deng

We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets.

Facial Expression Recognition

Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild

no code implementations CVPR 2017 Shan Li, Weihong Deng, JunPing Du

Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world.

Cannot find the paper you are looking for? You can Submit a new open access paper.