{"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/n-imagenet-towards-robust-fine-grained-object-1","title":"N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras","arxiv_id":"2112.01041","date":"2021-12-02","proceeding":"ICCV 2021 10","authors":["Junho Kim","Jaehyeok Bae","Gangin Park","Dongsu Zhang","Young Min Kim"],"abstract":"We introduce N-ImageNet, a large-scale dataset targeted for robust, fine-grained object recognition with event cameras. The dataset is collected using programmable hardware in which an event camera consistently moves around a monitor displaying images from ImageNet. N-ImageNet serves as a challenging benchmark for event-based object recognition, due to its large number of classes and samples. We empirically show that pretraining on N-ImageNet improves the performance of event-based classifiers and helps them learn with few labeled data. In addition, we present several variants of N-ImageNet to test the robustness of event-based classifiers under diverse camera trajectories and severe lighting conditions, and propose a novel event representation to alleviate the performance degradation. To the best of our knowledge, we are the first to quantitatively investigate the consequences caused by various environmental conditions on event-based object recognition algorithms. N-ImageNet and its variants are expected to guide practical implementations for deploying event-based object recognition algorithms in the real world.","url_abs":"https://arxiv.org/abs/2112.01041v2","url_pdf":"https://arxiv.org/pdf/2112.01041v2.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":"n-imagenet-towards-robust-fine-grained-object-1","repo_url":"https://github.com/82magnolia/n_imagenet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"robust-classification","task_name":"Robust classification"}],"methods":[],"datasets_introduced":[{"slug":"n-imagenet","name":"N-ImageNet","full_name":"Large-Scale Dataset for Event-Based Object Recognition"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset":"N-ImageNet","model":"Event Spike Tensor","rank_in_archive_order":1,"of":9,"metrics":{"Accuracy (%)":"48.93"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset":"N-ImageNet","model":"DiST","rank_in_archive_order":2,"of":9,"metrics":{"Accuracy (%)":"48.43"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset":"N-ImageNet","model":"Sorted Time Surface","rank_in_archive_order":3,"of":9,"metrics":{"Accuracy (%)":"47.90"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset":"N-ImageNet","model":"Event Histogram","rank_in_archive_order":4,"of":9,"metrics":{"Accuracy (%)":"47.73"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset":"N-ImageNet","model":"HATS","rank_in_archive_order":5,"of":9,"metrics":{"Accuracy (%)":"47.14"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset":"N-ImageNet","model":"Binary Event Image","rank_in_archive_order":6,"of":9,"metrics":{"Accuracy (%)":"46.36"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset":"N-ImageNet","model":"Timestamp Image","rank_in_archive_order":7,"of":9,"metrics":{"Accuracy (%)":"45.86"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset":"N-ImageNet","model":"Event Image","rank_in_archive_order":8,"of":9,"metrics":{"Accuracy (%)":"45.77"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset":"N-ImageNet","model":"Time Surface","rank_in_archive_order":9,"of":9,"metrics":{"Accuracy (%)":"44.32"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet-mini","task":"Classification","dataset":"N-ImageNet (mini)","model":"Event Imge","rank_in_archive_order":1,"of":6,"metrics":{"Accuracy (%)":"61.42"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet-mini","task":"Classification","dataset":"N-ImageNet (mini)","model":"Event Histogram","rank_in_archive_order":2,"of":6,"metrics":{"Accuracy (%)":"61.02"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet-mini","task":"Classification","dataset":"N-ImageNet (mini)","model":"Timestamp Image","rank_in_archive_order":3,"of":6,"metrics":{"Accuracy (%)":"60.46"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet-mini","task":"Classification","dataset":"N-ImageNet (mini)","model":"DiST","rank_in_archive_order":4,"of":6,"metrics":{"Accuracy (%)":"59.74"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet-mini","task":"Classification","dataset":"N-ImageNet (mini)","model":"Sorted Time Surface","rank_in_archive_order":5,"of":6,"metrics":{"Accuracy (%)":"58.38"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-n-imagenet-mini","task":"Classification","dataset":"N-ImageNet (mini)","model":"Binary Event Image","rank_in_archive_order":6,"of":6,"metrics":{"Accuracy (%)":"53.52"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2112.01041","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}