Search Results for author: Zhibo Zhang

Found 7 papers, 3 papers with code

A new database of Houma Alliance Book ancient handwritten characters and classifier fusion approach

no code implementations13 Jul 2022 Xiaoyu Yuan, Zhibo Zhang, Yabo Sun, Zekai Xue, Xiuyan Shao, Xiaohua Huang

This paper proposes a new database of Houma Alliance Book ancient handwritten characters and a multi-modal fusion method to recognize ancient handwritten characters.

TransCAM: Transformer Attention-based CAM Refinement for Weakly Supervised Semantic Segmentation

1 code implementation14 Mar 2022 Ruiwen Li, Zheda Mai, Chiheb Trabelsi, Zhibo Zhang, Jongseong Jang, Scott Sanner

In this paper, we propose TransCAM, a Conformer-based solution to WSSS that explicitly leverages the attention weights from the transformer branch of the Conformer to refine the CAM generated from the CNN branch.

Weakly-Supervised Semantic Segmentation

ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification

1 code implementation28 Nov 2021 Zhibo Zhang, Jongseong Jang, Chiheb Trabelsi, Ruiwen Li, Scott Sanner, Yeonjeong Jeong, Dongsub Shim

Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification.

Adversarial Robustness Classification +2

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

no code implementations24 Aug 2018 Wenbin Zhang, Jianwu Wang, Daeho Jin, Lazaros Oreopoulos, Zhibo Zhang

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved.

General Classification

Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

2 code implementations KDD 2018 Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, Dik Lun Lee

Using online A/B test, we show that the online Click-Through-Rate (CTRs) are improved comparing to the previous recommendation methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.

Graph Embedding Recommendation Systems

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