The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.
LVIS: A Dataset for Large Vocabulary Instance Segmentation
Abstract
Progress on object detection is enabled by datasets that focus the research community’s attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced ‘el-vis’): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect 2.2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at http://www.lvisdataset.org.
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“We build our benchmark on the LVIS dataset (Gupta et al., 2019), known for its rich long-tail vocabulary and abundance of small, densely packed objects.”
From ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning (2025) · cites this paper as Gupta et al., 2019
In the citing paper
“LVIS. We construct our benchmark for open-world small and dense object grounding based on the LVIS (Gupta et al., 2019) dataset, which offers the richest long-tail object vocabulary and the highest prevalence of small and densely packed instances among existing segmentation datasets.”
From ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning (2025) · cites this paper as Gupta et al., 2019
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“ADE20K[117] BBBC038v1[12] Cityscapes[25] DOORS[80] DRAM[24] EgoHOS[113] GTEA[34,63] Hypersim[86] IBD[17] iShape[111] LVIS[44] NDD20[100] NDISPark[22,23] OVIS[81] PPDLS[74] Plittersdorf[46] STREETS[91] TimberSeg[38] TrashCan[52] VISOR[28,27] WoodScape[112] PIDRay[104] ZeroWaste-f[6] Figure 8: Samples from the 23 divers…”
BackgroundMethodResultInfluentialSelf-citationScalability in Perception for Autonomous Driving: Waymo Open Dataset
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Metadata
- Authors
- Gupta, A., Dollar, P., & Girshick, R.
- Affiliations
- Meta (United States) (US)
- FAIR
- ORCID
- Gupta, Agrim
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 5351-5359
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2948672349
- Cited by
- 1,274
- References
- 28
- Citations by year
- 2026: 153 · 2025: 246 · 2024: 285 · 2023: 219 · 2022: 164 · 2021: 141 · 2020: 55 · 2019: 11
- FWCI
- 30.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- lymphovascular invasion, instance segmentation masks, object categories, object detection, benchmark dataset, complex scenes
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