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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The LVIS dataset is known for its rich long-tail vocabulary and abundance of small, densely packed objects.
“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
The LVIS dataset offers the richest long-tail object vocabulary and the highest prevalence of small and densely packed instances among existing segmentation datasets.
“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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Open-Vocabulary DETR with Conditional Matching
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A survey on few-shot class-incremental learning
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Probabilistic two-stage detectionPreprint
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Balanced MSE for Imbalanced Visual Regression
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KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA
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Self-citationGLaMM: Pixel Grounding Large Multimodal Model
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Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation
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Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection
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Cut and Learn for Unsupervised Object Detection and Instance Segmentation
Wang, X., Girdhar, R., Yu, S. X., & Misra, I. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00305 · Cited by 202 · see citing works
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CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation
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Towards Open Vocabulary Learning: A Survey
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OASIS: Only Adversarial Supervision for Semantic Image Synthesis
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Language-Grounded Indoor 3D Semantic Segmentation in the Wild
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The Devil Is in Classification: A Simple Framework for Long-Tail Instance Segmentation
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Mask Transfiner for High-Quality Instance Segmentation
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FocalClick: Towards Practical Interactive Image Segmentation
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MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation
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PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images
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On Bridging Generic and Personalized Federated Learning for Image ClassificationPreprint
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SiamMOT: Siamese Multi-Object Tracking
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A Survey on Long-Tailed Visual Recognition
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TAO: A Large-Scale Benchmark for Tracking Any Object
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RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
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Occluded Video Instance Segmentation: A Benchmark
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MixFormer: Mixing Features across Windows and Dimensions
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QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking
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ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
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Deep Polarization Cues for Transparent Object Segmentation
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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