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Efficient DETR: Improving End-to-End Object Detector with Dense Prior

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arxiv 2104.01318 v1 pith:CIHPMTKJ submitted 2021-04-03 cs.CV

Zhuyu Yao , Jiangbo Ai , Boxun Li , Chi Zhang This is my paper
classification cs.CV
keywords objectdetrefficientdecoderdetectiondenseend-to-endstructure
verification ladder T0 review T1 audit T2 compute T3 formal
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The recently proposed end-to-end transformer detectors, such as DETR and Deformable DETR, have a cascade structure of stacking 6 decoder layers to update object queries iteratively, without which their performance degrades seriously. In this paper, we investigate that the random initialization of object containers, which include object queries and reference points, is mainly responsible for the requirement of multiple iterations. Based on our findings, we propose Efficient DETR, a simple and efficient pipeline for end-to-end object detection. By taking advantage of both dense detection and sparse set detection, Efficient DETR leverages dense prior to initialize the object containers and brings the gap of the 1-decoder structure and 6-decoder structure. Experiments conducted on MS COCO show that our method, with only 3 encoder layers and 1 decoder layer, achieves competitive performance with state-of-the-art object detection methods. Efficient DETR is also robust in crowded scenes. It outperforms modern detectors on CrowdHuman dataset by a large margin.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 160 citations worldwide. Full citation record

  1. DQEN: Dual Query Enhancement Network for DETR-based HOI Detection

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    DQEN enhances DETR-based HOI detection by initializing object and interaction queries with encoder features and CLIP text-derived semantics, improving HICO-Det mAP but not V-COCO.

  2. DS-Det: Single-Query Paradigm and Attention Disentangled Learning for Flexible Object Detection

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    A flexible-query transformer detector that separates cross-attention localization from self-attention deduplication reports consistent accuracy gains over DINO across five backbones.

  3. Decoupled PROB: Decoupled Query Initialization Tasks and Objectness-Class Learning for Open World Object Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Decoupled PROB combines query selection with learnable queries and stops objectness prediction early in the decoder, improving known-object detection in open-world benchmarks.

  4. Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Cross-DINO improves small-object detection in DETR-like detectors by mixing MLP backbone features, a cross-coding fusion module, and a category-size soft-label loss, achieving 36.4% APs on COCO.

  5. HiddenObject: Modality-Agnostic Fusion for Multimodal Hidden Object Detection

    cs.CV 2025-08 conditional novelty 4.0 of 10

    A Mamba-based fusion network with a channel-aware decoder reports competitive or state-of-the-art results on RGB-thermal and RGB-depth hidden-object detection benchmarks.

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