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OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion

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arxiv 2407.07844 v2 pith:BLLD7HTD submitted 2024-07-10 cs.CV

Hao Wang , Pengzhen Ren , Zequn Jie , Xiao Dong , Chengjian Feng , Yinlong Qian , Lin Ma , Dongmei Jiang
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Yaowei Wang Xiangyuan Lan Xiaodan Liang
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classification cs.CV
keywords ov-dinodetectionfusionlanguage-awaredataopen-vocabularyunifiedselective
verification ladder T0 review T1 audit T2 compute T3 formal
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Open-vocabulary detection is a challenging task due to the requirement of detecting objects based on class names, including those not encountered during training. Existing methods have shown strong zero-shot detection capabilities through pre-training and pseudo-labeling on diverse large-scale datasets. However, these approaches encounter two main challenges: (i) how to effectively eliminate data noise from pseudo-labeling, and (ii) how to efficiently leverage the language-aware capability for region-level cross-modality fusion and alignment. To address these challenges, we propose a novel unified open-vocabulary detection method called OV-DINO, which is pre-trained on diverse large-scale datasets with language-aware selective fusion in a unified framework. Specifically, we introduce a Unified Data Integration (UniDI) pipeline to enable end-to-end training and eliminate noise from pseudo-label generation by unifying different data sources into detection-centric data format. In addition, we propose a Language-Aware Selective Fusion (LASF) module to enhance the cross-modality alignment through a language-aware query selection and fusion process. We evaluate the performance of the proposed OV-DINO on popular open-vocabulary detection benchmarks, achieving state-of-the-art results with an AP of 50.6% on the COCO benchmark and 40.1% on the LVIS benchmark in a zero-shot manner, demonstrating its strong generalization ability. Furthermore, the fine-tuned OV-DINO on COCO achieves 58.4% AP, outperforming many existing methods with the same backbone. The code for OV-DINO is available at https://github.com/wanghao9610/OV-DINO.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dynamic-DINO: Fine-Grained Mixture of Experts Tuning for Real-time Open-Vocabulary Object Detection

    cs.CV 2025-07 conditional novelty 7.0 of 10

    MoE fine-tuning with decomposed pre-trained FFN experts lets a real-time open-vocabulary detector beat a much larger-data baseline with similar active parameter count.

  2. Teaching MLLMs to Say No: Generalized Referring Expression Comprehension via Refusal Calibrated GRPO

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A recalibrated GRPO reinforcement learning method lets multimodal LLMs say 'None' for nonexistent referring expressions without sacrificing localization accuracy on objects that do exist.

  3. Open-Vocabulary Gaze Object Prediction: Benchmark and Method

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A COCO+GazeFollow-derived benchmark (86 categories) plus a Grounding DINO + gaze-selection pipeline with selective tuning improves open-vocabulary gaze object prediction over existing closed-vocabulary methods.

  4. FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model

    cs.CV 2025-10 conditional novelty 6.0 of 10

    A two-stage bilingual CLIP-style model with region-text supervision and a new text-side contrastive loss outperforms prior open models on fine-grained vision-language tasks in English and Chinese.

  5. ROVI: A VLM-LLM Re-Captioned Dataset for Open-Vocabulary Instance-Grounded Text-to-Image Generation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    ROVI's pre-detection VLM-LLM re-captioning yields richer open-vocabulary box labels on 1M curated images, and a GLIGEN model trained on ROVI improves instance grounding, prompt fidelity, and aesthetic quality in the p...

  6. Advancing Visual Large Language Model for Multi-granular Versatile Perception

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A 1.3B visual language model, MVP-LM, unifies word-based and sentence-based box and mask perception in one architecture and reports competitive benchmark scores.

  7. VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Fusing open-set and open-ended query sets raises LVIS detection accuracy, with the largest gains on rare categories, and lets one model run in either mode.

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