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Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head

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arxiv 2403.06892 v2 pith:DD76S5J4 submitted 2024-03-11 cs.CV cs.CL

Tiancheng Zhao , Peng Liu , Xuan He , Lu Zhang , Kyusong Lee This is my paper · ORCID
classification cs.CVcs.CL
keywords detectionreal-timeobjectomdetomdet-turboperformancetransformer-basedachieves
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End-to-end transformer-based detectors (DETRs) have shown exceptional performance in both closed-set and open-vocabulary object detection (OVD) tasks through the integration of language modalities. However, their demanding computational requirements have hindered their practical application in real-time object detection (OD) scenarios. In this paper, we scrutinize the limitations of two leading models in the OVDEval benchmark, OmDet and Grounding-DINO, and introduce OmDet-Turbo. This novel transformer-based real-time OVD model features an innovative Efficient Fusion Head (EFH) module designed to alleviate the bottlenecks observed in OmDet and Grounding-DINO. Notably, OmDet-Turbo-Base achieves a 100.2 frames per second (FPS) with TensorRT and language cache techniques applied. Notably, in zero-shot scenarios on COCO and LVIS datasets, OmDet-Turbo achieves performance levels nearly on par with current state-of-the-art supervised models. Furthermore, it establishes new state-of-the-art benchmarks on ODinW and OVDEval, boasting an AP of 30.1 and an NMS-AP of 26.86, respectively. The practicality of OmDet-Turbo in industrial applications is underscored by its exceptional performance on benchmark datasets and superior inference speed, positioning it as a compelling choice for real-time object detection tasks. Code: \url{https://github.com/om-ai-lab/OmDet}

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Forward citations

Cited by 9 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. DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    DDStereo uses two lightweight decoder branches sharing object queries plus a compact disparity extractor to deliver SOTA closed- and open-set accuracy with real-time inference on stereo 3D benchmarks.

  3. Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A multi-stage diffusion-based framework that generates labeled synthetic aerial images from weak image-level labels improves cross-domain vehicle detection AP50 over prior adaptation methods.

  4. 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.

  5. Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection

    cs.CV 2025-04 conditional novelty 6.0 of 10

    The Mcity Data Engine provides an open-source, end-to-end data engine whose ensemble of open-vocabulary detectors selected 100 frames that improved VRU detection mAP@0.5 by 17.45% in one iteration.

  6. CP-DETR: Concept Prompt Guide DETR Toward Stronger Universal Object Detection

    cs.CV 2024-12 conditional novelty 6.0 of 10

    CP-DETR combines progressive and gated multi-scale prompt-image fusion with text, visual, and optimized prompts to reach state-of-the-art universal detection with one model weight.

  7. C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes

    cs.CV 2026-07 conditional novelty 5.5 of 10

    Iterative LLM caption refinement guided by minority-class AP@0.5 lifts rare-object detection on frozen open-vocabulary detectors without labels or weight updates.

  8. Towards Real-Time Open-Vocabulary Video Instance Segmentation

    cs.CV 2024-12 conditional novelty 5.0 of 10

    TROY-VIS makes open-vocabulary video instance segmentation run in real time (around 25 FPS) while keeping accuracy comparable to or better than slower state-of-the-art models.

  9. DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding

    cs.CV 2024-11 conditional novelty 4.0 of 10

    DINO-X Pro sets new zero-shot detection records on COCO and LVIS, with large gains on rare classes, by scaling grounding pre-training and adding multiple prompt types and perception heads.

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