{"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/exploring-object-centric-and-scene-centric","title":"Exploring object-centric and scene-centric CNN features and their complementarity for human rights violations recognition in images","arxiv_id":"1805.04714","date":"2018-05-12","proceeding":null,"authors":["Grigorios Kalliatakis","Shoaib Ehsan","Ales Leonardis","Klaus McDonald-Maier"],"abstract":"Identifying potential abuses of human rights through imagery is a novel and\nchallenging task in the field of computer vision, that will enable to expose\nhuman rights violations over large-scale data that may otherwise be impossible.\nWhile standard databases for object and scene categorisation contain hundreds\nof different classes, the largest available dataset of human rights violations\ncontains only 4 classes. Here, we introduce the `Human Rights Archive Database'\n(HRA), a verified-by-experts repository of 3050 human rights violations\nphotographs, labelled with human rights semantic categories, comprising a list\nof the types of human rights abuses encountered at present. With the HRA\ndataset and a two-phase transfer learning scheme, we fine-tuned the\nstate-of-the-art deep convolutional neural networks (CNNs) to provide human\nrights violations classification CNNs (HRA-CNNs). We also present extensive\nexperiments refined to evaluate how well object-centric and scene-centric CNN\nfeatures can be combined for the task of recognising human rights abuses. With\nthis, we show that HRA database poses a challenge at a higher level for the\nwell studied representation learning methods, and provide a benchmark in the\ntask of human rights violations recognition in visual context. We expect this\ndataset can help to open up new horizons on creating systems able of\nrecognising rich information about human rights violations. Our dataset, codes\nand trained models are available online at\nhttps://github.com/GKalliatakis/Human-Rights-Archive-CNNs.","url_abs":"http://arxiv.org/abs/1805.04714v1","url_pdf":"http://arxiv.org/pdf/1805.04714v1.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":"exploring-object-centric-and-scene-centric","repo_url":"https://github.com/GKalliatakis/Human-Rights-Archive-CNNs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[{"slug":"hra","name":"HRA","full_name":"Human Rights Archive Database"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}