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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.20622 |
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| _version_ | 1866908653891616768 |
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| author | Ruan, Chi Zhao, Jiying Chen, Wenhu |
| author_facet | Ruan, Chi Zhao, Jiying Chen, Wenhu |
| contents | Although open-vocabulary object detectors can generalize to unseen categories, they still rely on predefined textual prompts or classifier heads during inference. Recent generative object detectors address this limitation by coupling an autoregressive language model with a detector backbone, enabling direct category name generation for each detected object. However, this straightforward design introduces structural redundancy and substantial latency. In this paper, we propose a Real-Time Generative Detection Transformer (RTGen), a real-time generative object detector with a succinct encoder-decoder architecture. Specifically, we introduce a novel Region-Language Decoder (RL-Decoder) that jointly decodes visual and textual representations within a unified framework. The textual side is organized as a Directed Acyclic Graph (DAG), enabling non-autoregressive category naming. Benefiting from these designs, RTGen-R34 achieves 131.3 FPS on T4 GPUs, over 270x faster than GenerateU. Moreover, our models learn to generate category names directly from detection labels, without relying on external supervision such as CLIP or pretrained language models, achieving efficient and flexible open-ended detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20622 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RTGen: Real-Time Generative Detection Transformer Ruan, Chi Zhao, Jiying Chen, Wenhu Computer Vision and Pattern Recognition Although open-vocabulary object detectors can generalize to unseen categories, they still rely on predefined textual prompts or classifier heads during inference. Recent generative object detectors address this limitation by coupling an autoregressive language model with a detector backbone, enabling direct category name generation for each detected object. However, this straightforward design introduces structural redundancy and substantial latency. In this paper, we propose a Real-Time Generative Detection Transformer (RTGen), a real-time generative object detector with a succinct encoder-decoder architecture. Specifically, we introduce a novel Region-Language Decoder (RL-Decoder) that jointly decodes visual and textual representations within a unified framework. The textual side is organized as a Directed Acyclic Graph (DAG), enabling non-autoregressive category naming. Benefiting from these designs, RTGen-R34 achieves 131.3 FPS on T4 GPUs, over 270x faster than GenerateU. Moreover, our models learn to generate category names directly from detection labels, without relying on external supervision such as CLIP or pretrained language models, achieving efficient and flexible open-ended detection. |
| title | RTGen: Real-Time Generative Detection Transformer |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.20622 |