YOLO-World: Real-Time Open-Vocabulary Object Detection
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Cited by 76 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation, we introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically, we propose a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset, YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore, the fine-tuned YOLO-World achieves remarkable performance on several downstream tasks, including object detection and open-vocabulary instance segmentation.
Key facts. Cited by 503 papers in the map (data through 14 Oct 2026). Builds on 53 of its 69 references that are in the map. First posted on arXiv 30 Jan 2024.
Builds on
53 of 69 references.
References · 53
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- 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,217
- 2021Grounded Language-Image Pre-trainingcited by 1,156
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
- 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
- 2021Open-vocabulary Object Detection via Vision and Language Knowledge Distillationcited by 906
- 2021MDETR -- Modulated Detection for End-to-End Multi-Modal Understandingcited by 819
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- 2021RepVGG: Making VGG-style ConvNets Great Againcited by 522
- 2020Open-Vocabulary Object Detection Using Captionscited by 427
- 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
- 2023Scaling Open-Vocabulary Object Detectioncited by 343
- 2022Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Modelcited by 317
- 2022Simple Open-Vocabulary Object Detection with Vision Transformerscited by 305
- 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
- 2022Open-Vocabulary DETR with Conditional Matchingcited by 220
- 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
- 2022DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detectioncited by 176
- 2022F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Modelscited by 150
- 2023Aligning Bag of Regions for Open-Vocabulary Object Detectioncited by 119
- 2022Learning Object-Language Alignments for Open-Vocabulary Object Detectioncited by 96
- 2023DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignmentcited by 90
- 2021Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labelingcited by 84
- 2022PP-YOLOE: An evolved version of YOLOcited by 75
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