YOLOE: Real-time seeing anything
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
YOLOE26 instantiates the open-vocabulary formulation of YOLOE on the YOLO26 detector.
“we extend the YOLO26 family to open-vocabulary scenarios by building YOLOE26 , which instantiates the open-vocabulary formulation of YOLOE [79] on the YOLO26 detector.”
From Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models (2026) · cites this paper as [79]
YOLOE extends the YOLO framework with embedding-based classification to support text-prompted, visual-prompted, and prompt-free inference within a single model.
“YOLOE [79] extends the YOLO framework with embedding-based classification to support text-prompted, visual-prompted, and prompt-free inference within a single model.”
From Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models (2026) · cites this paper as [79]
YOLOE-26 applies the YOLOE formulation to the YOLO26 detector with an upgraded text encoder, a pseudo-label data engine, and decoupled segmentation training, reaching 40.6 AP on LVIS minival under text prompting.
“YOLOE-26 , an open-vocabulary extension that applies the YOLOE [79] formulation to the YOLO26 detector with an upgraded text encoder, a pseudo-label data engine, and decoupled segmentation training, reaching 40.6 AP on LVIS minival under text prompting.”
From Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models (2026) · cites this paper as [79]
YOLOE-26 instantiates the open-vocabulary formulation of YOLOE on top of the YOLO26 detector family, retaining the same three inference modes while introducing four modifications.
“YOLOE-26 instantiates the open-vocabulary formulation of YOLOE [79] on top of the YOLO26 detector family. It retains the same three inference modes while introducing four modifications.”
From Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models (2026) · cites this paper as [79]
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References 54 · Cited by 65
- Authors
- Wang, A., Liu, L., Chen, H., Lin, Z., Han, J., & Ding, G.
- Affiliations
- Tsinghua University (CN)
- Venue
- 2025 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2025-10-19
- Pages
- 24591-24602
- OpenAlex
- W7160116129
- Cited by
- 65
- References
- 54
- Citations by year
- 2026: 65
- FWCI
- 48.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (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
- object detection, real-time detection, YOLO
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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.
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