This page lists the reproducibility papers that were accepted at the 2020 edition of ACM Multimedia, Seattle, United States.
- Title: Reproducibility Companion Paper: Instance of Interest Detection
- Authors: Fan Yu, DanDan Wang, Haonan Wang, Tongwei Ren, Jinhui Tang, Gangshan Wu, Jingjing Chen, Michael Riegler
- Abstract: To support the replication of “Instance of Interest Detection”, which was presented at MM’19, this companion paper provides the details of the artifacts. Instance of Interest Detection (IOID) aims to provide instance-level user interest modeling for image semantic description. In this paper, we explain the file structure of the source code and publish the details of our IOID dataset, which can be used to retrain the model with custom parameters. We also provide a program for component analysis to help other researchers to do experiments with alternative models that are not included in our experiments. Moreover, we provide a demo program for using our model easily.
- DOI: 10.1145/3394171.3414811
- Original ACM MM’19 Contribution: Instance of Interest Detection
- Result Reproduced:
- Title: Reproducibility Companion Paper: Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network
- Authors: Xin Wang, Bo Wu, Yueqi Zhong, Wei Hu, Jan Zahálka
- Abstract:This companion paper supports the experimental replication of paper “Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network”, which is presented at ACM Multimedia 2019. We provide the software package for replicating the implementation of Multi-Layered Comparison Network (MCN), as well as the Polyvore-T dataset and baseline methods compared in the original paper. This paper contains the guides to reproduce the experiment results including outfit compatibility prediction, outfit diagnosis and automatic outfit revision.
- DOI: 10.1145/3394171.3414812
- Original ACM MM’19 Contribution: Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network
- Result Reproduced:
- Title: Reproducibility Companion Paper: Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN
- Authors: Quoc-Tuan Truong, Hady W. Lauw, Martin Aumüller, Naoko Nitta
- Abstract:We revisit our contributions on visual sentiment analysis for online review images published at ACM Multimedia 2017, where we develop item-oriented and user-oriented convolutional neural networks that better capture the interaction of image features with specific expressions of users or items. In this work, we outline the experimental claims as well as describe the procedures to reproduce the results therein. In addition, we provide artifacts including data sets and code to replicate the experiments.
- DOI: 10.1145/3394171.3414813
- Original ACM MM’17 Contribution: Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN
- Result Reproduced:
- Title: Reproducibility Companion Paper: Selective Deep Convolutional Features for Image Retrieval
- Authors: Tuan NA Hoang, Thanh-Toan Do, Ngai-Man Cheung, Michael Riegler, Jan Zahálka
- Abstract:In this companion paper, firstly, we briefly summarize the contributions of our main manuscript: Selective Deep Convolutional Features for Image Retrieval, published in ACM MultiMedia 2017. In addition, we provide detail instructions together with pre-configured MATLAB scripts which allow experiments to be executed and to reproduce the results reported in our main manuscript effortlessly. The source code is available at https://github.com/hnanhtuan/selectiveConvFeatures_ACMMM_reproducibility.
- DOI: 10.1145/3394171.3414814
- Original ACM MM’17 Contribution: Selective Deep Convolutional Features for Image Retrieval
- Result Reproduced:
