Pith. sign in

REVIEW 13 cited by

LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2406.03459 v1 pith:FIUJJU4D submitted 2024-06-05 cs.CV

Qiang Chen , Xiangbo Su , Xinyu Zhang , Jian Wang , Jiahui Chen , Yunpeng Shen , Chuchu Han , Ziliang Chen
show 7 more authors
Weixiang Xu Fanrong Li Shan Zhang Kun Yao Errui Ding Gang Zhang Jingdong Wang
This is my paper
classification cs.CV
keywords encoderfeaturedetectionlw-detrmapsreal-timeapproachinterleaved
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this paper, we present a light-weight detection transformer, LW-DETR, which outperforms YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder, a projector, and a shallow DETR decoder. Our approach leverages recent advanced techniques, such as training-effective techniques, e.g., improved loss and pretraining, and interleaved window and global attentions for reducing the ViT encoder complexity. We improve the ViT encoder by aggregating multi-level feature maps, and the intermediate and final feature maps in the ViT encoder, forming richer feature maps, and introduce window-major feature map organization for improving the efficiency of interleaved attention computation. Experimental results demonstrate that the proposed approach is superior over existing real-time detectors, e.g., YOLO and its variants, on COCO and other benchmark datasets. Code and models are available at (https://github.com/Atten4Vis/LW-DETR).

Discussion (0). Sign in to comment.

Forward citations

Cited by 13 Pith papers

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

  1. LocAnyMed: Vision-Language Grounding for Multimodal Medical Images

    cs.CV 2026-08 conditional novelty 7.0 of 10

    A unified 210K medical grounding dataset and full fine-tuning raise a general model's localization F1 from 10.6 to 85.6 on held-out data and improve cross-source transfer.

  2. WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    WUTDet is a 100K-image ship detection dataset with benchmarks indicating Transformer models outperform CNN and Mamba architectures in accuracy and small-object detection for complex maritime environments.

  3. SAM 3: Segment Anything with Concepts

    cs.CV 2025-11 unverdicted novelty 7.0 of 10

    SAM 3 introduces promptable concept segmentation that doubles accuracy of prior systems on images and videos while improving standard SAM segmentation performance.

  4. Probing the 3D Object-Level Understanding of Pre-Trained Detection Transformers

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Frozen DETR-family object embeddings encode enough depth and 3D-location information that trained probes come close to specialized 3D detection models.

  5. Enabling Fully Integer-Only Inference for Lightweight Detection Transformers

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A fully integer-only lightweight DETR with split-scale projectors, sign-dependent ShiftGELU, and constrained Shiftmax cuts size ~3.6× and compute >10× at moderate mAP cost.

  6. Real-Time Source-Free Object Detection

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    RT-SFOD adapts dual-head detectors like YOLOv10 for source-free object detection via DHF pseudo-label fusion and MARD loss, delivering 1.4-3.5% mAP gains with 1.3x higher throughput and ~2x fewer parameters than prior...

  7. LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection

    cs.CV 2025-12 conditional novelty 6.0 of 10

    LeAD-M3D reports state-of-the-art monocular 3D detection accuracy on KITTI, Waymo, and Rope3D at real-time speeds using mixup-based denoising distillation, 3D-aware matching, and confidence-gated inference.

  8. ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A procedural scratch-rendering pipeline plus benchmark showing synthetic pre-training and fine-tuning outperforms real-only training for edge defect detection.

  9. Hippocampus-DETR: An Explicit Memory Object Detection Framework Based on Hippocampus Modeling

    cs.CV 2026-06 unverdicted novelty 5.0 of 10

    Hippocampus-DETR integrates a hippocampal memory network (HipNet) into DETR to simulate brain subregions for pattern separation, completion, and improved detection accuracy plus generalization.

  10. Echo-{\alpha}: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretation

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    Echo-α integrates organ-specific detectors with global visual context via an invoke-and-reason agentic loop, trained on a nine-task curriculum plus sequential RL, to achieve superior grounding (56.73%/43.78% F1@0.5) a...

  11. ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection

    cs.CV 2026-07 conditional novelty 4.5 of 10

    Procedural synthetic scratch data plus fine-tuning or mixed training consistently beats real-only training for lightweight scratch detectors on glossy and matte objects.

  12. RT-SDGOD: Real-Time Single-Domain Generalized Object Detection

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

    RT-SDGDet applies one-to-many supervision, Discriminative Evidence Diversity Learning, and Dual-view Evidence Consistency Learning during training to reduce missed detections in real-time object detectors under unseen...

  13. Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

    YOLO26 presents a unified real-time vision model family with dual-head end-to-end design, new training components, and task-specific heads that reports improved mAP-latency tradeoffs on COCO and LVIS benchmarks across...

Pith tools