{"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/embracing-events-and-frames-with-hierarchical","title":"Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection","arxiv_id":"2407.12582","date":"2024-07-17","proceeding":null,"authors":["Hu Cao","Zehua Zhang","Yan Xia","Xinyi Li","Jiahao Xia","Guang Chen","Alois Knoll"],"abstract":"In frame-based vision, object detection faces substantial performance degradation under challenging conditions due to the limited sensing capability of conventional cameras. Event cameras output sparse and asynchronous events, providing a potential solution to solve these problems. However, effectively fusing two heterogeneous modalities remains an open issue. In this work, we propose a novel hierarchical feature refinement network for event-frame fusion. The core concept is the design of the coarse-to-fine fusion module, denoted as the cross-modality adaptive feature refinement (CAFR) module. In the initial phase, the bidirectional cross-modality interaction (BCI) part facilitates information bridging from two distinct sources. Subsequently, the features are further refined by aligning the channel-level mean and variance in the two-fold adaptive feature refinement (TAFR) part. We conducted extensive experiments on two benchmarks: the low-resolution PKU-DDD17-Car dataset and the high-resolution DSEC dataset. Experimental results show that our method surpasses the state-of-the-art by an impressive margin of $\\textbf{8.0}\\%$ on the DSEC dataset. Besides, our method exhibits significantly better robustness (\\textbf{69.5}\\% versus \\textbf{38.7}\\%) when introducing 15 different corruption types to the frame images. The code can be found at the link (https://github.com/HuCaoFighting/FRN).","url_abs":"https://arxiv.org/abs/2407.12582v2","url_pdf":"https://arxiv.org/pdf/2407.12582v2.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":"embracing-events-and-frames-with-hierarchical","repo_url":"https://github.com/hucaofighting/frn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-dsec","task":"Object Detection","dataset":"DSEC","model":"CAFR","rank_in_archive_order":1,"of":12,"metrics":{"mAP":"38.0"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-pku-ddd17-car","task":"Object Detection","dataset":"PKU-DDD17-Car","model":"CAFR","rank_in_archive_order":1,"of":14,"metrics":{"mAP50":"86.7"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2407.12582","atlas_url":"https://app.syntology.ai/?focus=2407.12582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.12582"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/hucaofighting/frn","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"cabefc33d0a1123b","entry":"time_since","repo":"hucaofighting/frn","repo_kind":"official","path":"train_dsec.py","file_url":"https://github.com/hucaofighting/frn/blob/HEAD/train_dsec.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cabefc33d0a1123b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}