{"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/openloris-object-a-dataset-and-benchmark","title":"OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning","arxiv_id":"1911.06487","date":"2019-11-15","proceeding":null,"authors":["Qi She","Fan Feng","Xinyue Hao","Qihan Yang","Chuanlin Lan","Vincenzo Lomonaco","Xuesong Shi","Zhengwei Wang","Yao Guo","Yimin Zhang","Fei Qiao","Rosa H. M. Chan"],"abstract":"The recent breakthroughs in computer vision have benefited from the availability of large representative datasets (e.g. ImageNet and COCO) for training. Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks. Fully retraining models each time a new task becomes available is infeasible due to computational, storage and sometimes privacy issues, while na\\\"{i}ve incremental strategies have been shown to suffer from catastrophic forgetting. It is crucial for the robots to operate continuously under open-set and detrimental conditions with adaptive visual perceptual systems, where lifelong learning is a fundamental capability. However, very few datasets and benchmarks are available to evaluate and compare emerging techniques. To fill this gap, we provide a new lifelong robotic vision dataset (\"OpenLORIS-Object\") collected via RGB-D cameras. The dataset embeds the challenges faced by a robot in the real-life application and provides new benchmarks for validating lifelong object recognition algorithms. Moreover, we have provided a testbed of $9$ state-of-the-art lifelong learning algorithms. Each of them involves $48$ tasks with $4$ evaluation metrics over the OpenLORIS-Object dataset. The results demonstrate that the object recognition task in the ever-changing difficulty environments is far from being solved and the bottlenecks are at the forward/backward transfer designs. Our dataset and benchmark are publicly available at at \\href{https://lifelong-robotic-vision.github.io/dataset/object}{\\underline{https://lifelong-robotic-vision.github.io/dataset/object}}.","url_abs":"https://arxiv.org/abs/1911.06487v2","url_pdf":"https://arxiv.org/pdf/1911.06487v2.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":"openloris-object-a-dataset-and-benchmark","repo_url":"https://github.com/sheqi/Continual_Learning_CV","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"openloris-object-a-dataset-and-benchmark","repo_url":"https://github.com/lifelong-robotic-vision/lifelong-object-recognition-challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"lifelong-learning","task_name":"Lifelong learning"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[{"slug":"openloris-object","name":"OpenLORIS-object","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1911.06487","atlas_url":"https://app.syntology.ai/?focus=1911.06487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06487"}},"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/lifelong-robotic-vision/lifelong-object-recognition-challenge","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sheqi/Continual_Learning_CV","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"summary":{"ran_draft_wrong":2,"ran":2,"unverified":1},"by_repo_kind":{"listed":{"samples":5,"ran":4,"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":"d9def42110729a85","entry":"conv1x1","repo":"sheqi/Continual_Learning_CV","repo_kind":"listed","path":"backbones/resnet.py","file_url":"https://github.com/sheqi/Continual_Learning_CV/blob/HEAD/backbones/resnet.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"d9def42110729a85"}},{"code_sha256_prefix":"160bb14bd76201b4","entry":"conv3x3","repo":"sheqi/Continual_Learning_CV","repo_kind":"listed","path":"backbones/resnet.py","file_url":"https://github.com/sheqi/Continual_Learning_CV/blob/HEAD/backbones/resnet.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"code_sha256_prefix":"046c51a013eb21a5","entry":"get_multitask_experiment","repo":"sheqi/Continual_Learning_CV","repo_kind":"listed","path":"data.py","file_url":"https://github.com/sheqi/Continual_Learning_CV/blob/HEAD/data.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"046c51a013eb21a5"}},{"code_sha256_prefix":"a99bc1039b57a869","entry":"linearExcitability","repo":"sheqi/Continual_Learning_CV","repo_kind":"listed","path":"excitability_modules.py","file_url":"https://github.com/sheqi/Continual_Learning_CV/blob/HEAD/excitability_modules.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"a99bc1039b57a869"}},{"code_sha256_prefix":"e6fd4d30c58afef7","entry":"resnet18","repo":"sheqi/Continual_Learning_CV","repo_kind":"listed","path":"backbones/resnet.py","file_url":"https://github.com/sheqi/Continual_Learning_CV/blob/HEAD/backbones/resnet.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"e6fd4d30c58afef7"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}