Search Results for author: Liu Ren

Found 16 papers, 5 papers with code

Self-supervised Semantic Segmentation Grounded in Visual Concepts

no code implementations25 Mar 2022 Wenbin He, William Surmeier, Arvind Kumar Shekar, Liang Gou, Liu Ren

In this work, we propose a self-supervised pixel representation learning method for semantic segmentation by using visual concepts (i. e., groups of pixels with semantic meanings, such as parts, objects, and scenes) extracted from images.

Representation Learning Self-Supervised Learning +1

Interactive Visual Pattern Search on Graph Data via Graph Representation Learning

no code implementations18 Feb 2022 Huan Song, Zeng Dai, Panpan Xu, Liu Ren

GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints.

Graph Representation Learning

Unsupervised Discriminative Learning of Sounds for Audio Event Classification

no code implementations19 May 2021 Sascha Hornauer, Ke Li, Stella X. Yu, Shabnam Ghaffarzadegan, Liu Ren

Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet.

Classification Transfer Learning

Improving the Unsupervised Disentangled Representation Learning with VAE Ensemble

no code implementations1 Jan 2021 Nanxiang Li, Shabnam Ghaffarzadegan, Liu Ren

We show both theoretically and experimentally, the VAE ensemble objective encourages the linear transformations connecting the VAEs to be trivial transformations, aligning the latent representations of different models to be "alike".

Disentanglement

VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection

no code implementations27 Sep 2020 Liang Gou, Lincan Zou, Nanxiang Li, Michael Hofmann, Arvind Kumar Shekar, Axel Wendt, Liu Ren

In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications.

Autonomous Driving Decision Making +1

Visualizing Classification Structure of Large-Scale Classifiers

1 code implementation12 Jul 2020 Bilal Alsallakh, Zhixin Yan, Shabnam Ghaffarzadegan, Zeng Dai, Liu Ren

We propose a measure to compute class similarity in large-scale classification based on prediction scores.

Classification General Classification

Improve Unsupervised Domain Adaptation with Mixup Training

1 code implementation3 Jan 2020 Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, Liu Ren

Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain.

Domain Generalization Human Activity Recognition +2

Disentangled Representation Learning with Sequential Residual Variational Autoencoder

no code implementations ICLR 2020 Nanxiang Li, Shabnam Ghaffarzadegan, Liu Ren

Recent advancements in unsupervised disentangled representation learning focus on extending the variational autoencoder (VAE) with an augmented objective function to balance the trade-off between disentanglement and reconstruction.

Disentanglement

Controlling the Amount of Verbatim Copying in Abstractive Summarization

1 code implementation23 Nov 2019 Kaiqiang Song, Bingqing Wang, Zhe Feng, Liu Ren, Fei Liu

In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones.

Abstractive Text Summarization Language Modelling

Interpretable and Steerable Sequence Learning via Prototypes

2 code implementations23 Jul 2019 Yao Ming, Panpan Xu, Huamin Qu, Liu Ren

The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain.

Sentiment Analysis

An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data

no code implementations10 May 2019 Takanori Fujiwara, Jia-Kai Chou, Shilpika, Panpan Xu, Liu Ren, Kwan-Liu Ma

We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.

Dimensionality Reduction

Do Convolutional Neural Networks Learn Class Hierarchy?

no code implementations17 Oct 2017 Bilal Alsallakh, Amin Jourabloo, Mao Ye, Xiaoming Liu, Liu Ren

We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data.

Image Classification

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