CVPR WorkshopsPub Date : 2019-05-02DOI: 10.1609/AAAI.V34I07.6863
Zhongang Qi, S. Khorram, Fuxin Li
{"title":"Visualizing Deep Networks by Optimizing with Integrated Gradients","authors":"Zhongang Qi, S. Khorram, Fuxin Li","doi":"10.1609/AAAI.V34I07.6863","DOIUrl":"https://doi.org/10.1609/AAAI.V34I07.6863","url":null,"abstract":"Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular approach for visualizing and understanding deep networks. However, heatmaps that do not correlate with the network may mislead human, hence the performance of heatmaps in providing a faithful explanation to the underlying deep network is crucial. In this paper, we propose I-GOS, which optimizes for a heatmap so that the classification scores on the masked image would maximally decrease. The main novelty of the approach is to compute descent directions based on the integrated gradients instead of the normal gradient, which avoids local optima and speeds up convergence. Compared with previous approaches, our method can flexibly compute heatmaps at any resolution for different user needs. Extensive experiments on several benchmark datasets show that the heatmaps produced by our approach are more correlated with the decision of the underlying deep network, in comparison with other state-of-the-art approaches.","PeriodicalId":277994,"journal":{"name":"CVPR Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130442118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR WorkshopsPub Date : 2006-06-17DOI: 10.1109/CVPRW.2006.153
M. Savvides, R. Abiantun, J. Heo, S. Park, C. Xie, B. Vijayakumar
{"title":"Partial & Holistic Face Recognition on FRGC-II data using Support Vector Machine","authors":"M. Savvides, R. Abiantun, J. Heo, S. Park, C. Xie, B. Vijayakumar","doi":"10.1109/CVPRW.2006.153","DOIUrl":"https://doi.org/10.1109/CVPRW.2006.153","url":null,"abstract":"In this paper we investigate how to perform face recognition on the hardest experiment (Exp4) in Face Recognition Grand Challenge(FRGC) phase-II data which deals with subjects captured under uncontrolled conditions such as harsh overhead illumination, some pose variations and facial expressions in both indoor and outdoor environments. Other variations include the presence and absence of eye-glasses. The database consists of a generic dataset of 12,776 images for training a generic face subspace, a target set of 16,028 images and a query set of 8,014 images are given for matching. We propose to use our novel face recognition algorithm using Kernel Correlation Feature Analysis for dimensionality reduction (222 features) coupled with Support Vector Machine discriminative training in the Target KCFA feature set for providing a similarity distance measure of the probe to each target subject. We show that this algorithm configuration yields the best verification rate at 0.1% FAR (87.5%) compared to PCA+SVM, GSLDA+SVM, SVM+SVM, KDA+SVM. Thus we explore with our proposed algorithm which facial regions provide the best discrimination ability, we analyze performing partial face recognition using the eye-region, nose region and mouth region. We empirically find that the eye-region is the most discriminative feature of the faces in FRGC data and yields a verification rate closest to the holistic face recognition of 83.5% @ 0.1% FAR compared to 87.5%. We use Support Vector Machines for fusing these two to boost the performance to ~90@0.1 % FAR on the first large-scale face database such as the FRGC dataset.","PeriodicalId":277994,"journal":{"name":"CVPR Workshops","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117155807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CVPR WorkshopsPub Date : 2004-06-27DOI: 10.1109/CVPR.2004.375
R. Hammoud, R. McMillan
{"title":"Joint IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum","authors":"R. Hammoud, R. McMillan","doi":"10.1109/CVPR.2004.375","DOIUrl":"https://doi.org/10.1109/CVPR.2004.375","url":null,"abstract":"","PeriodicalId":277994,"journal":{"name":"CVPR Workshops","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121094946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}