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YOLO-World: Real-Time Open-Vocabulary Object Detection

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arxiv 2401.17270 v3 pith:B6L6IBGY submitted 2024-01-30 cs.CV

Tianheng Cheng , Lin Song , Yixiao Ge , Wenyu Liu , Xinggang Wang , Ying Shan This is my paper
classification cs.CV
keywords yolo-worlddetectionobjectopen-vocabularyachievesvision-languageyoloaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 28 citations worldwide. Full citation record

  1. 3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection

    cs.CV 2025-07 conditional novelty 7.0 of 10

    3D-MOOD is the first end-to-end monocular 3D object detector for open-set classes and novel scenes, achieving SOTA on Omni3D and on new open-set benchmarks.

  2. UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation

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    UniMC uses tokenized instance conditions (class, box, keypoints) and a timestep-aware modulator in a DiT backbone to control multi-class human and animal image generation, trained and evaluated on the new HAIG-2.9M dataset.

  3. ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation

    cs.HC 2026-08 conditional novelty 6.0 of 10

    An LLM-agent artwork annotation system that combines proactive label suggestions with interaction-driven skill learning reported roughly 50% faster annotation and higher label agreement in a 12-participant study.

  4. Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG

    cs.IR 2026-07 conditional novelty 6.0 of 10

    A master–satellite edge station pairs MAX78000/02 always-on visual/acoustic sentinels with selective Jetson multimodal RAG, local species ID, and multi-agent reporting to cut energy and uplink cost.

  5. VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors

    cs.CV 2025-10 conditional novelty 6.0 of 10

    An IoU-weighted entropy objective and image-conditioned prompt selection adapt YOLO-World and Grounding DINO at test time, improving robustness on style, weather, low-light, and corruption shifts without labels.

  6. Can We Challenge Open-Vocabulary Object Detectors with Generated Content in Street Scenes?

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Inpainting unusual objects into street scenes reveals that open-vocabulary detectors miss objects based on image location rather than object semantics.

  7. Towards Domain-Generalized Open-Vocabulary Object Detection: A Progressive Domain-invariant Cross-modal Alignment Method

    cs.CV 2026-03 conditional novelty 5.0 of 10

    A progressive curriculum that trains open-vocabulary detectors on low-ambiguity, high-signal cross-modal alignments first improves robustness to visual domain shifts, with modest, test-tuned gains.

  8. MSNav: Zero-Shot Vision-and-Language Navigation with Dynamic Memory and LLM Spatial Reasoning

    cs.CV 2025-08 conditional novelty 5.0 of 10

    MSNav integrates dynamic map pruning, fine-tuned spatial reasoning (Qwen-Sp), and GPT-4o planning to improve zero-shot vision-and-language navigation on R2R and REVERIE.

  9. Policy-Driven Transfer Learning in Resource-Limited Animal Monitoring

    cs.CV 2025-09 reject novelty 3.0 of 10

    Using a UCB bandit selection, the framework identifies RTDETRx as the best pre-trained model for animal detection with F1=0.718, versus 0.690 for exhaustive selection, while running fewer models per image.

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