{"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/feanet-feature-enhanced-attention-network-for","title":"FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation","arxiv_id":"2110.08988","date":"2021-10-18","proceeding":null,"authors":["Fuqin Deng","Hua Feng","Mingjian Liang","Hongmin Wang","Yong Yang","Yuan Gao","Junfeng Chen","Junjie Hu","Xiyue Guo","Tin Lun Lam"],"abstract":"The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level features from both the channel and spatial views. Benefited from the proposed FEAM module, our FEANet can preserve the spatial information and shift more attention to high-resolution features from the fused RGB-T images. Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU). For the 480 x 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card.","url_abs":"https://arxiv.org/abs/2110.08988v1","url_pdf":"https://arxiv.org/pdf/2110.08988v1.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":"feanet-feature-enhanced-attention-network-for","repo_url":"https://github.com/matrixgame2018/FEANet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"real-time-semantic-segmentation","task_name":"Real-Time Semantic Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"thermal-image-segmentation","task_name":"Thermal Image Segmentation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-fmb-dataset","task":"Semantic Segmentation","dataset":"FMB Dataset","model":"FEANet (RGB-Infrared)","rank_in_archive_order":14,"of":14,"metrics":{"mIoU":"46.80"},"uses_additional_data":false},{"leaderboard":"/sota/thermal-image-segmentation-on-mfn-dataset","task":"Thermal Image Segmentation","dataset":"MFN Dataset","model":"FEANet","rank_in_archive_order":32,"of":55,"metrics":{"mIOU":"55.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2110.08988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.08988"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/matrixgame2018/FEANet","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_violates":1,"unverified":4},"by_repo_kind":{"official":{"samples":5,"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":"5f4f4355be28c01e","entry":"isnan","repo":"matrixgame2018/FEANet","repo_kind":"official","path":"loss_hub/losses/lovasz.py","file_url":"https://github.com/matrixgame2018/FEANet/blob/HEAD/loss_hub/losses/lovasz.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5f4f4355be28c01e"}},{"code_sha256_prefix":"1b4fef545c17040d","entry":"focal_loss_with_logits","repo":"matrixgame2018/FEANet","repo_kind":"official","path":"loss_hub/losses/_functional.py","file_url":"https://github.com/matrixgame2018/FEANet/blob/HEAD/loss_hub/losses/_functional.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1b4fef545c17040d"}},{"code_sha256_prefix":"ab905d7b509bc70c","entry":"mean","repo":"matrixgame2018/FEANet","repo_kind":"official","path":"loss_hub/losses/lovasz.py","file_url":"https://github.com/matrixgame2018/FEANet/blob/HEAD/loss_hub/losses/lovasz.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ab905d7b509bc70c"}},{"code_sha256_prefix":"b898a0aa68830b14","entry":"softmax_focal_loss_with_logits","repo":"matrixgame2018/FEANet","repo_kind":"official","path":"loss_hub/losses/_functional.py","file_url":"https://github.com/matrixgame2018/FEANet/blob/HEAD/loss_hub/losses/_functional.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b898a0aa68830b14"}},{"code_sha256_prefix":"5322bd0251d8da57","entry":"to_tensor","repo":"matrixgame2018/FEANet","repo_kind":"official","path":"loss_hub/losses/_functional.py","file_url":"https://github.com/matrixgame2018/FEANet/blob/HEAD/loss_hub/losses/_functional.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5322bd0251d8da57"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}