Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection

• URL: http://arxiv.org/abs/2507.07994v3
• Date: Sun, 27 Jul 2025 18:58:28 GMT
• Title: Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
• Abstract summary: Keypoint detection faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable.<n>This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative.<n>Our proposed framework overcomes these hurdles with a prototypical setup, combined with a grid-based locator and prototypical domain adaptation.
• Score: 80.90808879991182
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable. This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative. However, challenges arise in mastering cross-modal embeddings and handling user-specific sketch styles. Our proposed framework overcomes these hurdles with a prototypical setup, combined with a grid-based locator and prototypical domain adaptation. We also demonstrate success in few-shot convergence across novel keypoints and classes through extensive experiments.

Related papers

• TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration [74.97459526627395]
  We introduce TIDE, a template-guided iterative framework with two complementary mechanisms.<n>We propose iterative discovery, which surfaces a small batch of candidates per round while conditioning on what has already been found.<n>We show substantial gains over single-shot and parallel multi-agent baselines on task coverage, identification, and resolution.
  arXiv  Detail & Related papers  (2026-06-03T11:23:08Z)
• Leveraging Hierarchical Image-Text Misalignment for Universal Fake Image Detection [58.927873049646024]
  We show that fake images cannot be properly aligned with corresponding captions compared to real images.<n>We propose a simple yet effective ITEM by leveraging the image-text misalignment in a joint visual-language space as discriminative clues.
  arXiv  Detail & Related papers  (2025-11-01T06:51:14Z)
• Generative Compositor for Few-Shot Visual Information Extraction [60.663887314625164]
  We propose a novel generative model, named Generative generative spatialtor, to address the challenge of few-shot VIE.<n>Generative generative spatialtor is a hybrid pointer-generator network that emulates the operations of a compositor by retrieving words from the source text.<n>The proposed method achieves highly competitive results in the full-sample training, while notably outperforms the baseline in the 1-shot, 5-shot, and 10-shot settings.
  arXiv  Detail & Related papers  (2025-03-21T04:56:24Z)
• Keypoint Promptable Re-Identification [76.31113049256375]
  Occluded Person Re-Identification (ReID) is a metric learning task that involves matching occluded individuals based on their appearance. We introduce Keypoint Promptable ReID (KPR), a novel formulation of the ReID problem that explicitly complements the input bounding box with a set of semantic keypoints. We release custom keypoint labels for four popular ReID benchmarks. Experiments on person retrieval, but also on pose tracking, demonstrate that our method systematically surpasses previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-07-25T15:20:58Z)
• Open-Vocabulary Animal Keypoint Detection with Semantic-feature Matching [74.75284453828017]
  Open-Vocabulary Keypoint Detection (OVKD) task is innovatively designed to use text prompts for identifying arbitrary keypoints across any species. We have developed a novel framework named Open-Vocabulary Keypoint Detection with Semantic-feature Matching (KDSM) This framework combines vision and language models, creating an interplay between language features and local keypoint visual features.
  arXiv  Detail & Related papers  (2023-10-08T07:42:41Z)
• A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning [29.511632089649552]
  We propose a novel URL framework for multivariate time series by learning time-series-specific shapelet-based representation. To the best of our knowledge, this is the first work that explores the shapelet-based embedding in the unsupervised general-purpose representation learning. A unified shapelet-based encoder and a novel learning objective with multi-grained contrasting and multi-scale alignment are particularly designed to achieve our goal.
  arXiv  Detail & Related papers  (2023-05-30T09:31:57Z)
• Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints [8.390939268280235]
  Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. We propose DALF, a novel deformation-aware network for jointly detecting and describing keypoints. Our approach also enhances the performance of two real-world applications: deformable object retrieval and non-rigid 3D surface registration.
  arXiv  Detail & Related papers  (2023-04-02T18:01:51Z)
• I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketches [74.63313641583602]
  We propose a method to generate a potential grasp configuration relevant to the sketch-depicted objects. Our model is trained and tested in an end-to-end manner which is easy to be implemented in real-world applications.
  arXiv  Detail & Related papers  (2022-05-09T04:23:36Z)
• Weakly Supervised Keypoint Discovery [27.750244813890262]
  We propose a method for keypoint discovery from a 2D image using image-level supervision. Motivated by the weakly-supervised learning approach, our method exploits image-level supervision to identify discriminative parts. Our approach achieves state-of-the-art performance for the task of keypoint estimation on the limited supervision scenarios.
  arXiv  Detail & Related papers  (2021-09-28T01:26:53Z)
• Deep Self-Supervised Representation Learning for Free-Hand Sketch [51.101565480583304]
  We tackle the problem of self-supervised representation learning for free-hand sketches. Key for the success of our self-supervised learning paradigm lies with our sketch-specific designs. We show that the proposed approach outperforms the state-of-the-art unsupervised representation learning methods.
  arXiv  Detail & Related papers  (2020-02-03T16:28:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.