2 code implementations • 22 Dec 2018 • Aravind Sankar, Yanhong Wu, Liang Gou, Wei zhang, Hao Yang
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization.
no code implementations • 23 Sep 2020 • Chin-Chia Michael Yeh, Dhruv Gelda, Zhongfang Zhuang, Yan Zheng, Liang Gou, Wei zhang
Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database.
no code implementations • 27 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.
no code implementations • 3 Jan 2022 • Arvind Kumar Shekar, Laureen Lake, Liang Gou, Liu Ren
It is on this space we estimate the novelty of the test samples.
no code implementations • 25 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.
no code implementations • CVPR 2023 • Wenbin He, Suphanut Jamonnak, Liang Gou, Liu Ren
To further improve the pixel embeddings and enable language-driven semantic segmentation, we design two types of consistency guided by vision-language models: 1) embedding consistency, aligning our pixel embeddings to the joint feature space of a pre-trained vision-language model, CLIP; and 2) semantic consistency, forcing our model to make the same predictions as CLIP over a set of carefully designed target classes with both known and unknown prototypes.
no code implementations • 1 May 2023 • Wenbin He, Suphanut Jamonnak, Liang Gou, Liu Ren
To further improve the pixel embeddings and enable language-driven semantic segmentation, we design two types of consistency guided by vision-language models: 1) embedding consistency, aligning our pixel embeddings to the joint feature space of a pre-trained vision-language model, CLIP; and 2) semantic consistency, forcing our model to make the same predictions as CLIP over a set of carefully designed target classes with both known and unknown prototypes.
2 code implementations • 25 Jun 2023 • Thang Doan, Xin Li, Sima Behpour, Wenbin He, Liang Gou, Liu Ren
We argue that this contextual information should already be embedded within the known classes.
no code implementations • 6 Nov 2023 • Jinbin Huang, Wenbin He, Liang Gou, Liu Ren, Chris Bryan
Deep learning models are widely used in critical applications, highlighting the need for pre-deployment model understanding and improvement.
no code implementations • 12 Jan 2024 • Xiwei Xuan, Jorge Piazentin Ono, Liang Gou, Kwan-Liu Ma, Liu Ren
Data slice-finding is an emerging technique for evaluating machine learning models.
2 code implementations • 10 Mar 2024 • Thang Doan, Sima Behpour, Xin Li, Wenbin He, Liang Gou, Liu Ren
Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting.