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Detect Everything with Few Examples

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arxiv 2309.12969 v4 pith:AY5NDLOO submitted 2023-09-22 cs.CV

Xinyu Zhang , Yuhan Liu , Yuting Wang , Abdeslam Boularias This is my paper
classification cs.CV
keywords de-vitfew-shotnovelobjectrobotsotabenchmarkscoco
verification ladder T0 review T1 audit T2 compute T3 formal
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Few-shot object detection aims at detecting novel categories given only a few example images. It is a basic skill for a robot to perform tasks in open environments. Recent methods focus on finetuning strategies, with complicated procedures that prohibit a wider application. In this paper, we introduce DE-ViT, a few-shot object detector without the need for finetuning. DE-ViT's novel architecture is based on a new region-propagation mechanism for localization. The propagated region masks are transformed into bounding boxes through a learnable spatial integral layer. Instead of training prototype classifiers, we propose to use prototypes to project ViT features into a subspace that is robust to overfitting on base classes. We evaluate DE-ViT on few-shot, and one-shot object detection benchmarks with Pascal VOC, COCO, and LVIS. DE-ViT establishes new state-of-the-art results on all benchmarks. Notably, for COCO, DE-ViT surpasses the few-shot SoTA by 15 mAP on 10-shot and 7.2 mAP on 30-shot and one-shot SoTA by 2.8 AP50. For LVIS, DE-ViT outperforms few-shot SoTA by 17 box APr. Further, we evaluate DE-ViT with a real robot by building a pick-and-place system for sorting novel objects based on example images. The videos of our robot demonstrations, the source code and the models of DE-ViT can be found at https://mlzxy.github.io/devit.

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Cited by 7 Pith papers

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

  1. Visual Textualization for Image Prompted Object Detection

    cs.CV 2025-06 conditional novelty 7.0 of 10

    Visual textualization projects support images into the text feature space and prompts an unmodified OVLM, achieving strong few-shot and open-set detection results.

  2. Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection

    cs.CV 2026-08 conditional novelty 6.0 of 10

    VLM-synthesized foreground and background images plus feature noise improve few-shot detection accuracy on six cross-domain benchmarks.

  3. VFM-Guided Semi-Supervised Detection Transformer under Source-Free Constraints for Remote Sensing Object Detection

    cs.CV 2025-08 conditional novelty 6.0 of 10

    VG-DETR combines a mean-teacher detector with DINOv2-guided pseudo-label mining and dual-level feature alignment, reporting 77.5% mAP on xView to DOTA and 70.6% on HRRSD to SSDD.

  4. Free-Lunch Augmentation by Revisiting Diffusion-Based Data Generation for Cross-Domain Few-Shot Object Detection

    cs.CV 2026-08 conditional novelty 5.0 of 10

    SITN improves cross-domain few-shot detection and segmentation by using weakened-noise diffusion and background inpainting to synthesize helpful training images, outperforming prior methods on all reported benchmarks.

  5. MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering

    cs.CL 2025-08 unverdicted novelty 5.0 of 10

    LLMs handle factual lookups on mobility trajectories well but perform far worse on reasoning and explanation questions in the new 5,800-pair MobQA benchmark.

  6. Few-Shot Object Detection via Spatial-Channel State Space Model

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A Mamba-based channel sequence model combined with spatial attention improves few-shot object detection on VOC and COCO.

  7. NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning

    cs.CV 2025-07 reject novelty 4.0 of 10

    NexViTAD fuses Hiera and DINOv2 features with a multi-task decoder and Sinkhorn K-means memory bank to detect industrial defects across domains, reporting MVTec AD target AUC of 97.5%.

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