GenCape: Structure-Inductive Generative Modeling for Category-Agnostic Pose Estimation

• URL: http://arxiv.org/abs/2605.13151v1
• Date: Wed, 13 May 2026 08:17:20 GMT
• Title: GenCape: Structure-Inductive Generative Modeling for Category-Agnostic Pose Estimation
• Abstract summary: Category-agnostic pose estimation (CAPE) aims to localize keypoints on query images from arbitrary categories.<n>We propose GenCape, a Generative-based framework for CAPE that infers keypoint relationships solely from image-based support inputs.
• Score: 22.606848016741107
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Category-agnostic pose estimation (CAPE) aims to localize keypoints on query images from arbitrary categories, using only a few annotated support examples for guidance. Recent approaches either treat keypoints as isolated entities or rely on manually defined skeleton priors, which are costly to annotate and inherently inflexible across diverse categories. Such oversimplification limits the model's capacity to capture instance-wise structural cues critical for accurate pixel-level localization. To overcome these limitations, we propose GenCape, a Generative-based framework for CAPE that infers keypoint relationships solely from image-based support inputs, without additional textual descriptions or predefined skeletons. Our framework consists of two principal components: an iterative Structure-aware Variational Autoencoder (i-SVAE) and a Compositional Graph Transfer (CGT) module. The former infers soft, instance-specific adjacency matrices from support features through variational inference, embedded layer-wise into the Graph Transformer Decoder for progressive structural priors refinement. The latter adaptively aggregates multiple latent graphs into a query-aware structure via Bayesian fusion and attention-based reweighting, enhancing resilience to visual uncertainty and support-induced bias. This structure-aware design facilitates effective message propagation among keypoints and promotes semantic alignment across object categories with diverse keypoint topologies. Experimental results on the MP-100 dataset show that our method achieves substantial gains over graph-support baselines under both 1- and 5-shot settings, while maintaining competitive performance against text-support counterparts.

Related papers

• DAS-PMVC: A Framework for Partial Multi-View Clustering via Dual Alignment and Structure Enhancement [13.05750989297313]
  This paper proposes a framework for partial multi-view clustering via dual alignment and structure enhancement (DAS-PMVC)<n>DAS-PMVC includes three parts: textbfanchor graph structure alignment, textbfstructure-enhanced feature learning, and textbfa dual alignment strategy<n> Experimental results on various datasets demonstrate that the DAS-PMVC framework outperforms existing state-of-the-art methods in clustering performance.
  arXiv  Detail & Related papers  (2026-07-30T07:01:35Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• Domain Generalization through Spatial Relation Induction over Visual Primitives [4.085285915236828]
  Primitive-Aware Structure for domain gEneralization (PARSE) is an image classification framework that factors visual recognition visual primitives and their relational composition.<n>We represent these compositions using soft binary, ternary, and quaternary predicates over primitive locations, yielding differentiable measures of spatial alignment that can be learned end-to-end.<n>Across CUB-DG and the DomainBed benchmark suite,PARSE improves accuracy by over 4.5 percentage points on CUB-DG and remains competitive with existing DG methods on DomainBed.
  arXiv  Detail & Related papers  (2026-05-07T11:31:46Z)
• Matching-Based Few-Shot Semantic Segmentation Models Are Interpretable by Design [8.993770750003673]
  Few-Shot Semantic (FSS) models achieve strong performance in segmenting novel classes with minimal labeled examples.<n>This paper introduces the first dedicated method for interpreting matching-based FSS models.<n>Our Affinity Explainer approach extracts attribution maps that highlight which pixels in support images contribute most to query segmentation predictions.
  arXiv  Detail & Related papers  (2025-11-22T19:22:10Z)
• Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings [67.5600169375126]
  We study the task of panoptic symbol spotting in computer-aided design (CAD) drawings composed of vector graphical primitives.<n>Existing methods typically rely on imageization, graph construction, or point-based representation.<n>We propose VecFormer, a novel method that addresses these challenges through line-based representation of primitives.
  arXiv  Detail & Related papers  (2025-05-29T12:33:11Z)
• "Principal Components" Enable A New Language of Images [79.45806370905775]
  We introduce a novel visual tokenization framework that embeds a provable PCA-like structure into the latent token space.<n>Our approach achieves state-of-the-art reconstruction performance and enables better interpretability to align with the human vision system.
  arXiv  Detail & Related papers  (2025-03-11T17:59:41Z)
• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing [66.6712018832575]
  Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the model's performance on unseen domains. We make use of large-scale VLMs like CLIP and leverage the textual feature to dynamically adjust the classifier's weights for exploring generalizable visual features.
  arXiv  Detail & Related papers  (2024-03-21T11:58:50Z)
• Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention [7.939095881813804]
  Few-shot segmentation aims to train a segmentation model that can fast adapt to a novel task for which only a few annotated images are provided. We introduce an instance-aware data augmentation (IDA) strategy that augments the support images based on the relative sizes of the target objects. The proposed IDA effectively increases the support set's diversity and promotes the distribution consistency between support and query images.
  arXiv  Detail & Related papers  (2024-01-18T10:29:10Z)
• Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network [28.46908214462594]
  Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples. We propose a Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate latent context structure in query images.
  arXiv  Detail & Related papers  (2023-01-09T08:00:01Z)
• Self-Supervised Tuning for Few-Shot Segmentation [82.32143982269892]
  Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. Existing meta-learning method tends to fail in generating category-specifically discriminative descriptor when the visual features extracted from support images are marginalized in embedding space. This paper presents an adaptive framework tuning, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme.
  arXiv  Detail & Related papers  (2020-04-12T03:53:53Z)

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.