{"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/learning-to-segment-the-tail","title":"Learning to Segment the Tail","arxiv_id":"2004.00900","date":"2020-04-02","proceeding":"CVPR 2020 6","authors":["Xinting Hu","Yi Jiang","Kaihua Tang","Jingyuan Chen","Chunyan Miao","Hanwang Zhang"],"abstract":"Real-world visual recognition requires handling the extreme sample imbalance in large-scale long-tailed data. We propose a \"divide&conquer\" strategy for the challenging LVIS task: divide the whole data into balanced parts and then apply incremental learning to conquer each one. This derives a novel learning paradigm: class-incremental few-shot learning, which is especially effective for the challenge evolving over time: 1) the class imbalance among the old-class knowledge review and 2) the few-shot data in new-class learning. We call our approach Learning to Segment the Tail (LST). In particular, we design an instance-level balanced replay scheme, which is a memory-efficient approximation to balance the instance-level samples from the old-class images. We also propose to use a meta-module for new-class learning, where the module parameters are shared across incremental phases, gaining the learning-to-learn knowledge incrementally, from the data-rich head to the data-poor tail. We empirically show that: at the expense of a little sacrifice of head-class forgetting, we can gain a significant 8.3% AP improvement for the tail classes with less than 10 instances, achieving an overall 2.0% AP boost for the whole 1,230 classes.","url_abs":"https://arxiv.org/abs/2004.00900v2","url_pdf":"https://arxiv.org/pdf/2004.00900v2.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":"learning-to-segment-the-tail","repo_url":"https://github.com/JoyHuYY1412/LST_LVIS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"incremental-learning","task_name":"Incremental Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.00900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.00900"}},"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/JoyHuYY1412/LST_LVIS","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1,"unverified":3},"by_repo_kind":{"official":{"samples":4,"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":4,"samples":[{"code_sha256_prefix":"e261fa29066b37e5","entry":"smooth_l1_loss","repo":"JoyHuYY1412/LST_LVIS","repo_kind":"official","path":"maskrcnn_benchmark/layers/smooth_l1_loss.py","file_url":"https://github.com/JoyHuYY1412/LST_LVIS/blob/HEAD/maskrcnn_benchmark/layers/smooth_l1_loss.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"mcp_get_code":{"code_sha256":"e261fa29066b37e5"}},{"code_sha256_prefix":"83c64313d0501174","entry":"get_group_gn","repo":"JoyHuYY1412/LST_LVIS","repo_kind":"official","path":"maskrcnn_benchmark/modeling/make_layers.py","file_url":"https://github.com/JoyHuYY1412/LST_LVIS/blob/HEAD/maskrcnn_benchmark/modeling/make_layers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"mcp_get_code":{"code_sha256":"83c64313d0501174"}},{"code_sha256_prefix":"2902bf4410253ff7","entry":"interpolate","repo":"JoyHuYY1412/LST_LVIS","repo_kind":"official","path":"maskrcnn_benchmark/layers/misc.py","file_url":"https://github.com/JoyHuYY1412/LST_LVIS/blob/HEAD/maskrcnn_benchmark/layers/misc.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":false,"mcp_get_code":{"code_sha256":"2902bf4410253ff7"}},{"code_sha256_prefix":"426fd02a2beaf826","entry":"sigmoid_focal_loss_cpu","repo":"JoyHuYY1412/LST_LVIS","repo_kind":"official","path":"maskrcnn_benchmark/layers/sigmoid_focal_loss.py","file_url":"https://github.com/JoyHuYY1412/LST_LVIS/blob/HEAD/maskrcnn_benchmark/layers/sigmoid_focal_loss.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":false,"mcp_get_code":{"code_sha256":"426fd02a2beaf826"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}