mlsanityData to 14 Oct 2026

Incremental Object Detection with Prompt-based Methods

arXiv 2508.14599 ↗First posted 20 Aug 2025cs.CVcited by 2

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Abstract (arXiv)

Visual prompt-based methods have seen growing interest in incremental learning (IL) for image classification. These approaches learn additional embedding vectors while keeping the model frozen, making them efficient to train. However, no prior work has applied such methods to incremental object detection (IOD), leaving their generalizability unclear. In this paper, we analyze three different prompt-based methods under a complex domain-incremental learning setting. We additionally provide a wide range of reference baselines for comparison. Empirically, we show that the prompt-based approaches we tested underperform in this setting. However, a strong yet practical method, combining visual prompts with replaying a small portion of previous data, achieves the best results. Together with additional experiments on prompt length and initialization, our findings offer valuable insights for advancing prompt-based IL in IOD.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 62 references that are in the map. First posted on arXiv 20 Aug 2025.

Builds on

39 of 62 references.

References · 39

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27 more
  1. 2022Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Modelcited by 317
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026MIVIFI: Bridging Perspective and Fisheye Domains for Training Multi-View Fisheye Image Generation Modelscited by 0
  2. 2026MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Expertscited by 0