{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/c3ae-exploring-the-limits-of-compact-model","title":"C3AE: Exploring the Limits of Compact Model for Age Estimation","arxiv_id":"1904.05059","date":"2019-04-10","proceeding":"CVPR 2019 6","authors":["Chao Zhang","Shuaicheng Liu","Xun Xu","Ce Zhu"],"abstract":"Age estimation is a classic learning problem in computer vision. Many larger\nand deeper CNNs have been proposed with promising performance, such as AlexNet,\nVggNet, GoogLeNet and ResNet. However, these models are not practical for the\nembedded/mobile devices. Recently, MobileNets and ShuffleNets have been\nproposed to reduce the number of parameters, yielding lightweight models.\nHowever, their representation has been weakened because of the adoption of\ndepth-wise separable convolution. In this work, we investigate the limits of\ncompact model for small-scale image and propose an extremely Compact yet\nefficient Cascade Context-based Age Estimation model(C3AE). This model\npossesses only 1/9 and 1/2000 parameters compared with MobileNets/ShuffleNets\nand VggNet, while achieves competitive performance. In particular, we re-define\nage estimation problem by two-points representation, which is implemented by a\ncascade model. Moreover, to fully utilize the facial context information,\nmulti-branch CNN network is proposed to aggregate multi-scale context.\nExperiments are carried out on three age estimation datasets. The\nstate-of-the-art performance on compact model has been achieved with a\nrelatively large margin.","url_abs":"http://arxiv.org/abs/1904.05059v2","url_pdf":"http://arxiv.org/pdf/1904.05059v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"c3ae-exploring-the-limits-of-compact-model","repo_url":"https://github.com/StevenBanama/C3AE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"age-estimation","task_name":"Age Estimation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"googlenet","method_name":"GoogLeNet"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"inception-module","method_name":"Inception Module"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/age-estimation-on-fgnet","task":"Age Estimation","dataset":"FGNET","model":"C3AE (WIKI-IMDB)","rank_in_archive_order":3,"of":8,"metrics":{"MAE":"2.95"},"uses_additional_data":false},{"leaderboard":"/sota/age-estimation-on-fgnet","task":"Age Estimation","dataset":"FGNET","model":"AEBFI","rank_in_archive_order":8,"of":8,"metrics":{"MAE":"52"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.05059","atlas_url":"https://app.syntology.ai/?focus=1904.05059","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}