# DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection

arXiv: https://arxiv.org/abs/2604.02753 · HTML: https://mlsanity.com/paper/2604.02753/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 87 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 24 of 24)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection](https://mlsanity.com/paper/2207.03482/) (2022)
- [Learning Object-Language Alignments for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2211.14843/) (2022)
- [Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation](https://mlsanity.com/paper/2203.10593/) (2022)
- [A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future](https://mlsanity.com/paper/2307.09220/) (2023)
- [LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models](https://mlsanity.com/paper/2501.18954/) (2025)
- [X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks](https://mlsanity.com/paper/2204.05626/) (2022)
- [LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors](https://mlsanity.com/paper/2402.04630/) (2024)
- [Learning to Prompt with Text Only Supervision for Vision-Language Models](https://mlsanity.com/paper/2401.02418/) (2024)
- [Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2305.03944/) (2023)
- [G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation](https://mlsanity.com/paper/2108.07482/) (2021)
- [Visual Variational Autoencoder Prompt Tuning](https://mlsanity.com/paper/2503.17650/) (2025)
- [Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2503.11005/) (2025)
- [Improving CLIP Robustness with Knowledge Distillation and Self-Training](https://mlsanity.com/paper/2309.10361/) (2023)

## Built on by (most cited first, 3 of 3)

- [EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer](https://mlsanity.com/paper/2605.01732/) (2026)
- [Though Language Models Err While They Strive: Conformal Prediction for Self-Correcting Scientific Generation](https://mlsanity.com/paper/2607.16704/) (2026)
- [Code Consistency Preference Optimization Verification for Language Model Alignment](https://mlsanity.com/paper/2609.19002/) (2026)

## Abstract (arXiv)

Open-vocabulary object detection (OVOD) enables models to recognize objects beyond predefined categories, but existing approaches remain limited in practical deployment. On the one hand, multimodal designs often incur substantial computational overhead due to their reliance on text encoders at inference time. On the other hand, tightly coupled training objectives introduce a trade-off between closed-set detection accuracy and open-world generalization. Thus, we propose Decoupled Cognition DETR (DeCo-DETR), a vision-centric framework that addresses these challenges through a unified decoupling paradigm. Instead of depending on online text encoding, DeCo-DETR constructs a hierarchical semantic prototype space from region-level descriptions generated by pre-trained LVLMs and aligned via CLIP, enabling efficient and reusable semantic representation. Building upon this representation, the framework further disentangles semantic reasoning from localization through a decoupled training strategy, which separates alignment and detection into parallel optimization streams. Extensive experiments on standard OVOD benchmarks demonstrate that DeCo-DETR achieves competitive zero-shot detection performance while significantly improving inference efficiency. These results highlight the effectiveness of decoupling semantic cognition from detection, offering a practical direction for scalable OVOD systems.