EdgeCape: Edge Weight Prediction For Category-Agnostic Pose Estimation
Authors: Or Hirschorn
Shai Avidan
Venue: ICLR 2026 Poster (2026)
Keywords: Category Agnostic Pose Estimation
Keypoint Localization
Few Shot Learning
2D Pose Estimation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: This paper introduces EdgeCape, a graph-based approach for category-agnostic pose estimation that predicts category-agnostic pose-graphs to achieve improved accuracy

Abstract: Category-Agnostic Pose Estimation (CAPE) localizes keypoints across diverse object categories with a single model, using one or few annotated support images. Recent works have shown that using a pose-graph (i.e., treating keypoints as nodes in a graph rather than isolated points) helps handle occlusions and break symmetry. However, these methods assume a given pose-graph with equal-weight edges, leading to suboptimal results. We introduce EdgeCape, a novel framework that overcomes these limitations by predicting the graph's edge weights in order to optimize localization. To further leverage structural (i.e., graph) priors, we propose integrating Markov Attention Bias, which modulates the self-attention interaction between nodes based on the number of hops between them. We show that this improves the model’s ability to capture global spatial dependencies. Evaluated on the MP-100 benchmark, which includes 100 categories and over 20K images, EdgeCape achieves state-of-the-art results in the 1-shot and 5-shot settings, significantly improving keypoint localization accuracy.

Published as a conference paper at ICLR 2026
EDGECAPE: EDGE WEIGHT PREDICTION FOR CATEGORY-AGNOSTIC POSE ESTIMATION
Or Hirschorn and Shai Avidan Tel Aviv University
https://orhir. githu.. 1o/ io/edge_cape
ABSTRACT
Category-Agmose Pose Estimation (CAPE) localizes keypoints across diverse object categories with single model, using one or few annotated support images. Recent works have shown that using pose-graph (i.e., treating keypoints as nodes in a graph rather than isolated points) helps handle occlusions and break symme- try. However, these methods assume a given pose-graph with equal-weight edges, leading to suboptimal results. We introduce EdgeCape, a novel framework that overcomes these limitations by predicting the graph's 's edge weights in order to op- timize localization. To further leverage structural (i.e., graph) priors, we propose integrating Markov Attention Bias, which modulates the self-attention interaction between nodes based the number of hops between them. We show that this improves the model's ability to capture global spatial dependencies. Evaluated on the MP-100 benchmark, which includes 100 categories and over 20K images, EdgeCape achieves state-of-the- results in the 1-shot and 5-shot settings, sig- nificantly improving localization accuracy. Our code is publicly available.
INTRODUCTION
2D pose estimation, which involves identifying the locations of key semantic parts within image. is fundamental problem in computer vision. From human pose estimation (Fang et al. 2022a
Cao et al.) 2019) Yang et al. 2021) to animal tracking and vehicle localization (Song et al. 2019 Reddy et al. 2018), accurate pose estimation is essential for both academic research and industrial applications. Traditional approaches mainly focused on category-specific models that are tailored to pre-defined keypoints and pre-defined object categories. These models achieve high accuracy in some domains but struggle when encountering categories that lack annotated training data. This limitation has sparked interest in flexible models that generalize beyond the fixed categories and keypoints seen in training.
To address these challenges, Category-Agnostic Pose Estimation (CAPE) has emerged as promis- ing solution (Xu et al. 2022a). CAPÉ enables keypoint localization for arbitrary keypoints and any object category using only few annotated support images, allowing single model to generalize across diverse object types. By significantly reducing the need for extensive data collection and re- training for each new category or keypoint definition, CAPE provides versatile and cost-effective approach to pose estimation. However, CAPE remains exceptionally challenging as it must infer keypoint relations that structurally meaningful across vastly different object categories using few annotated examples. This is crucial for breaking symmetry, handling occlusions, and preserving object structure. Figure hhihhlghts the differences between recent CAPE methods. Although early works treated keypoints isolated entities, recent works (Hirschorn & Avidan 2024| Rusaaovssy ett al., 2024) address this challenge by leveraging user-defined structural graphs. These graphs de- scribe the skeletal relations between keypoints and are termed prse-ssaphs. These structural priors hold for any viewing angle and both for rigid and non-rigid objects. Graph-based methods provide robustness to occlusions, handle symmetric structures, and enforce anatomical consistency.
While effective, these methods rely on user srovidrovided unweighted pose-graphs, significantly limit- ing their adaptability. For example, when locating human's right elbow, both the right hand and shoulder provide useful context, but their contributions should vary in strength. In conventional pose estimation, these relative contributions are learned by leveraging the fact that the object category is

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Support Data
CapeFormer (Only Nodes)
GraphCape (Unweighted Graph)
EdgeCape (Ours) (Weighted Graph)
Figure 1: Given support image, keypoints definition, and skeletal relations (support data) from any category, our model localizes the keypoints on a query image. Previous methods treat keypoints as isolated (CapeFormer (Shi et al., 2023b)) or use unweighted graphs (GraphCape (Hirschorn & Avidan 2024)) ). We, in contrast, predict weighted graphs that lead to better localization.
fixed, meaning its pose-graph definition is shared and can be learned from abundant same-category examples. contrast, CAPE presents fundamentally new and largely unexplored challenges: object categories are unknown, and keypoints are determined dynamically at test time. We aim to advance graph-based CAPE by leveraging optimal pose-graphs with real-valued edge weights. However, multiple challenges arise. First, humans struggle to determine the optimal pose-graph, as demon- strated in Figure [2] This is crucial, as different graph definitions significantly impact localization performance (Hirschorn & Avidan 2024). Moreover, requiring users to manually specify edge weights is impractical, as there is no clear correct assignment. Second, existing edge prediction methods are unsuitable for CAPE. Self-attention baaed approaches, such as Graph Attention Trans- formers Veličković et al., 2017), struggle in category- -agnosiic settings because they fail to model structural keypoint relations (Hirschorn & Avidan 2024). Alternatively, GCN variants (Cai et al.) 2019 Shi et al. 2019) assume fixed pose-graph an assumption that does not hold for CAPE. Lastly, CAPE requires generalization unseen object categories, making direct pose-graph predic- tion particularly challenging, as it demands anatomy, Thus, a more sophisticated approach is needed. see a In this paper, we introducé Edge a novel approach that extends the graph-based CAPE frame- pose-gray asse-graph prediction. Our goal is find the
implicit understanding of 3D object structure and
work by tackling the challenge of vategory-agnoste optimal weighted pose-graph graph for any class. Rather than inferring the entire pose-grape from scratch, we refine a user-provided unweighted pose-graph by learning to assign edge weights and add or re- move connections needed. This approach eliminates the need for 3D prior knowledge and enables the model to adapt to any object category with varying geometries using a simple structural prior. Furthermore, method learns subtle instance-specift adjustments, which are crucial to handling CAPE's inherent ambiguity. In addition, we overcome the lack of ground-truth optimal edge weights through a self-supervised strategy that predicts pose-graphy to maximize localization accuracy and improve occlusion handling. Finally, to better exploit pose-graphs for improved localization, we introduce Markov attention bias that adjusts the model's self-attention according the structural distance between nodes in the graph, supporting more complex spatial relations.
We evaluate approach the MP-100 benchmark, comprehensive dataset comprising over 20,000 images spanning 100 diverse categories. Our method outperforms the previous state-of- the-art, significantly improving localization accuracy. We also demonstrate the robustness of our approach under challenging conditions through an extensive ablation study.
In summary, we introduce a novel CAPE method that enables more nuanced capture of object geometry, leveraging optimal weighted pose-graphs to improve localization accuracy. Our contribu- tions can be summarized as follows:
We are the first to explore category-agnostic pose-graph prediction, introducing novel mechanism that learns instance-specific weighted graphs that generalize across unseen ob- ject categories.
We propose an enhanced graph-based CAPE architecture with Markov Attention Bias, al- lowing the model better capture complex spatial dependencies between keypoints.
We achieve SOTA results on the MP-100 benchmark, demonstrating the effectiveness of our approach in both 1-shot and 5-shot settings.
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Figure 2: Which of these pose-graphs is optimal? As edge placement can be ambiguous, we aim learn the optimal pose-graph for -agggor-agmostic keypoint localization. We show several graph annotations for the same image. The second-to-last (cyan) serves as input to our model; the last (orange) is our predicted pose-graph. While not visually superior, it achieves the best performance.
2
RELATED WORKS
2.1
SNTEGORY-ANOOSTE POSE ESTIMATION
Category-agnostic pose estimation (CAPE), introduced by Xu(Xu et al. 2022a). aims to extend conventional speecic pose estimation (Fang al. 2022a Cao et al. 2019 Yang et al. 2021| Yu et al. 2021: Yang et al., 2022) and multi-category pose estimation (Xu et al. 2022b Yu al., 2021) to unseen categories, enabling models that generalize beyond category training for greater flexibility and robustness. POMNet (Xu et al.] (022a), a regression- based ap- proach (Li et al. 2021 Oberweger & Lepetit 2017: Zimmermann & Brox 2017), utilized trans- former to encode query images and support keypoints for similarity prediction. Building on this,
CapeFormer (Shi et al. 2023a) adopted DETR-like framework (Carion et al. 2020 [Zhang al.,
2022 Fang et al. 2022b Wang et al. 2022) to address unreliable matching outcomes, refining imitial predictions. Later, different approaches were suggested to improve various aspects of CapeFormer. ESCAPE(Nguyen et al. 2024), introduced super-keypoints that capture the statistics of semantically
related keypoints from different categories, tackling variability in object appearances and poses. Chen (Chen et al. 2024) predicted meta-points independent of support annotations, later refined to
align with target keypoints. Similar to object proposal tokens (Alexe et al., 2012; Hosang et al.)
2013 Zitnick & Dollár 2014). meta-points also provide structural cues. SCAPE (Liang al. [024]]
simplified the task by directly regressing keypoint locations without refinement. X-Pose Yang et al,
2024) introduced a bottom-up approach for multi-instance localization. PPM (Peng et al. (2244 har-
nessed Stable Diffusion (Rombach et al. 2022), learning pseudo-prompts for each keypoint at test time and localizing via diffusion model rross-attention maps. This idea was later expanded Chenn et al. (2025b) which explored weak-shot keypoint estimation using intra-image and inter-image correspondences. FMMP (Chen al.. 2025a) employed multiscale features and leveraged denser keypoint localization by randomly mixing structurally linked keypoints. While effective, its reliance on multi-scale feature extraction restricts the range of backbone architectures that can be utilized. CapeLLM Kim al. (2025) uses text in order to locate keypoints, using LLMs to replace the need for support examples. Recent work highlights the importance of object structure for CAPE localiza- tion. GraphCape (Hirschorn & Avidan 2024) used graph convolutional networks (GCNs) (Kipf & Welling 2016) to model structural relations between keypoints, improving robustness to symmetry and occlusions. CapeX (Rusanovsky al. 2024) extended this by integrating pose-graphs with tex- tual point explanations, enabling pose inference from natural language. SDPNet (Ren et al. 2024) also adopted graph-based methods, employing keypoints. It predicts adjacency matrices via keypoint self-attention, supervised by secondary GCN and a mask-reconstruction task for self-supervised learning. However, these methods face key limitations: (1) they rely only on keypoint features, whereas object structure may be better inferred from the entire object; (2) generalizing to unseen categories without prior knowledge is highly challenging and requires 3D anatomical understanding; and (3) structural cues may be en- coded in the auxiliary GCN rather than the adjacency matrix, leading to information loss since the GCN is used only during training. In contrast, we combine image and keypoint features to refine prior structural knowledge and leverage decoder supervision to build a weighted graph optimized for localization. Although category-agnostic graph prediction remains underexplored. AutoLink He et al. 2022) validates the utility of learned weighted graphs for pose estimation. However, it is con- strained to category category-specifie setting and fixed skeletal topology. In contrast, CAPE necessitates category-agnostie generalization, adapting to varying object categories and keypoint definitions at test time.
auxiliary GCN for information sharing between
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Agetor
Powe-Graph
Predictor
Support Image
Backbone
Graph Transfomer
F/
Graph-Based Kaypoint Prediction
Query Image
Figure 3: Framework Overview. Our model consists of two main components: a pose-graph predictor (visualized in Figure [5) and a graph-based keypoint predictor. The pose-graph predictor refines the prior graph input by predicting residual connections. The graph-based keypoint predictor then utilizes the predicted keypoint relations, improving localization across diverse object structures.
2.2 STRUCTURAL ENCODING IN GNNS
Graph Transformers extend Transformers graph-structune data, where self-attention can be viewed as message passing between all nodes, independent of connectivity. Structural information incorporated using three main strategies:
Positional Embedding from Graph Structure. Mialon and Feldman Mialon et al. 2021; Feldman
al. 2022) introduce positional encodings based on graph kernels. Dwivedi (Dwivedi & Bresson,
2020 used Laplacian eigenvectors, later extended by Kreuzer (Kreuzer et al. 2021) with spectral
encodings for greater expressivity. Ma (Ma et al. 2023b) instead modeled adjacency matrices as stochastic processes, using random-walk probabilities as relative encodings.
Integrating Graph Neural Networks (GNNs) with Transformers. Wu (Wu et al. 2021) captured local features with GNNs followed by Transformers to model long-range dependen- cics. Rampášck (Rampášek et al. 2022) combined GNN and self-attention layers, while Hirschorn (Hirschorn & Avidan 2024 replaced Transformer feed-forward layers with GCNs.
Incorporating Graph Structural Bias into Self-Attention. Ying YYin et al. 2021) encodes shortest-path distances biases that directly alter the attention mechanism. Zhao (Zhao et al. 2021] incorporated proximity-base neighborhood relations. Dwivedi (Dwivedi & Bresson, (2020) integrated edge features into attention, while Wu al. (Wu al. 2022) injected topological infor- mation as relational biases to enhance attention fidelity.
3 METHOD
The full framework of our method is shown in Figure In the following, we outline the key com- ponents of EdgeCape: (1) graph-based CAPE formulation, (2) the category-agnostic pose-graph prediction network, and (3) an enhanced graph decoder that embeds structural biases. Additional details are the supplementary (Section
3.1 PRELIMINARIES
CapeFormer Shi et al. 2023b treats keypoints as individual entities, sharing information via self- attention. While effective for single-category pose estimation, it struggles generalize structural understanding to unseen categories (Hirschorn & Avidan, 2024). Consequently, self-attention alone insufficient to capture structural relations for CAPE. GraphCape (Hirschorn & Avidan (024)) addresses this limitation by incorporating user-provided pose-graphs structural prior. The pose- graph is represented by an adjacency matrix Aprior {0, 10 where aij 1 if node v; is connected to node U/, and 0 otherwise. Using graph convolutions. information is propagated be- tween connected keypoints, aiding in structure preservation and occlusion handling. Additionally, integrating graphs during training helps break feature symmetry by enforcing similarity based
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structural relations rather than purely semantic attributes. Our work is built GraphCape, and include the full framework details in the supplementary (Section
3.2 CATEGORY- POSE- GASEGRRAHH PREDICTION
-alegory-agnostic pose-graph prediction presents fundamentally new and largely unexplored chal- lenge: object structures vary widely across categories, making difficult to infer structure from scratch. Our key insight is predict adjustments the entire graph. This yields expressive, weighted pose-graphs that adapt to object-specific struc- tures while remaining grounded in prior knowledge. Formally, we define learnable function to learn the residual pose-graph in the form of an adjacency matrix AA E R
eser-provided prior Apeice rather than learning
AA So( (Aproo: Fsr F.
(1)
where Aprior {0, 1 is the unweighted graph input provided by the user (like in GraphCape), and l and F IR KxC are the support image and keypoint features.
Refining Structural Features. First, we refine the keypoint features F to enhance their struc- tural semantics. Rather than sharing information only among the keypoint features P&, we also incorporate the support image features F, to provide valuable global structural context, which is especially important in the category-agnostic setting where objects' orientations vary widely and keypoints alone may be ambiguous.
We build on a graph transformer decoder (Hirschorn Avidan 2024), which is well-suited for modeling structural dependencies. To strengthen the interaction between F and F., we add cross-attention layer where the image features are updated using the keypoint features. We refer to this as a attentittnon graph decoder, since it enables bidirectional exchange of structural informa- tion between image and keypoint features. practice, we find that refining image features alongside keypoint features significantly improves the quality of learned structural representations. The archi- tecture of our pose-graph prediction network is illustrated in the Supplementary (Figure L5].
This modification also changes the role of the decoder itself. Originally designed to exchange infor- mation between support keypoints and a query image, now jointly processes keypoints and support image features of the same image. This repurposing allows the model to learn richer structural priors directly from the support image, leveraging the fact that structural patterns are shared within object categories.
Predicting Residual Edges. as pairwise
Using the refined structure-aware features Ek fineeds we compute AA
AAG =< Fi fined:: refined
F
This similarity measure naturally quantifies the directional alignment and strength of relations be- tween keypoints. We found this similarity measure as a balance between accuracy and efficiency. Alternatives such as MLPs attention- basedd predictions yielded only marginal improvements at higher computational cost.
The resulting AA is then combined with the user-provided prior Aprior- A na've approach would directly sum Apric and A4, but this leads to unstable training and poor convergence, as the output graphs at the start of training lack structural meaning. Instead, we introduce simple yet effective scaling mechanism that stabilizes training. We combine Aprior and AA using learnable scaling factor initialized zero, and pass the result through an activation function to ensure positivity:
Rel.U(A Aprior + cAA)
where is the learnable scalar. At the start of training, 0, meaning the output pose graph is initially Aprior providing a stable foundation.
Lastly, enforee adjacency matrix symmetry and normalization ensure proper interpretation as a valid pose-graph, and right-stochasticity. Symmetry is enforced by averaging the matrix with its transpose and normalization is applied row-wise:
A = A' TT 2
Aij
(4)
À Σ, Aij
(5)
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Pose-Graph Prediction Self-Supervision. Training the pose-graph predietor with only a localiza- tion loss does not reliably produce useful graphs. We therefore introduce an additional supervision signal. Designing such signal is challenging: even for humans, identifying the correct graph struc- ture non-trivial, and assigning precise edge weights impractical. To address this, we adopt an unsupervised masking strategy, where our keypoint prediction module provides indirect supervision for the pose-graph predictor. Using masking, we aim to find adjacency matrices that can help the model overcome occlusions, realistic scenario for this task.
Optimization is done as follows. We randomly mask a subset of the input support keypoint features using the vector mask M, and replace them with learnable masking token Fmakk. Then, using the masked input, the model predicts the query keypoint locations:
(6) where o is the Hadamard product, de is the keypoints prediction module, pe are the predicted co- ordinates from the proposal generator (Section C and A is the predicted adjacency matrix. The adjacency loss is the L1 distance between the predicted locations pm using masked keypoint fea- tures and the ground-trune locations P:
p go [M o F + (1 MM Fmask: Po)
Ladj Σ P. P31
During backpropagation. this loss used to update only the adjacency predictor. To achieve this, freeze the decoder's weights and all inputs except A, ensuring that the adjacency matrix itself encodes information useful for overcoming occlusion.
The final training objective combines this adjacency loss with the standard localization loss:
== Loffect Audj Ludj
where Loffeet is L1 localization loss as in Shi et al. 2023b). More details about our training scheme are in the supplementary. Section C.4
3.3 MARKOV ATTENTION BIAS
In CAPE, encoding spatial relations between keypoints is critical for robust localization (Hirschorn & Avidan 2024 Rusanovsky et al., 2024). Having established way to predict more informative adjacency matrices, we now focus on how to better leverage these spatial relations during localiza- tion. Keypoint localization done using a transformer decoder (Figure 3 where the self-attention mechanism provides global receptive field by considering all pairwise keypoint interactions. How- ever, this flcxibility comes at a cost, as it weakens the influence of structural relations. We aim to improve the model's ability to capture both local and long-range structural dependencies. Graph- Cape (Hirschorn Avidan 2024) partially tackled this issue by integrating GCNs into the feed- forward network. While effective at modeling local structure, GCNs still struggle with reasoning over long-range relations and therefore do not provide a complete solution. To address this lim- itation, we introduce a new graph-informed bias term (Ying al., (021) into the self-attention mechanism, enabling structural connectivity patterns to directly influence the flow of information and enhance overall performance.
Our predicted adjacency matrix À contains weighted edges, reflecting varying strengths of keypoint relations. We normalize it row-wise, treating it stochastic matrix that defines Markov process. Under this view, (At);) represents the probability of reaching node Vy from node i/ in exactly k-hops. Aij captures direct (1-hop) links while (A*))y captures multi-hop (long-range) relations, providing principled way to encode both local and global structure. We start by constructing the matrix P:
P.j = P...
E
where K is the maximum number of hops considered. Intuitively, Psy captures multi-hop structural relations. Then, using an MLP: IR - IR we modulate the influence of keypoints based on graph distance, allowing the model to learn which relations are most relevant for localization. The resulting modified self-attention in the decoder is:
aij (h, Wx) + MLP(P.g)
(10)
Vd

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Support
GT
CapePromer
GraphCape
Ours
Figure 4: Qualitative Comparison. We visualize keypoint predictions for the 1-shot setting. The left column shows the support data, followed by ground-truth query keypoints, and results of differ- ent methods. Our method performs best by leveraging predicted weighted pose-graphs, which serve as more effective structural priors for keypoint localization.
This formulation preserves the expressiveness of self-attention while explicitly guiding it to respect structural priors. In effect, the model learns weigh keypoint interactions based both semantic similarity and graph connectivity. A figure of the new graph-transfone transformer decoder, along with the full derivation of the bias term and training scheme, are provided the supplementary. Section C.3
EXPERIMENTS
We follow standard CAPE practice by training and evaluating MP-100 dataset (Xu et al. 2022a) which includes over 20,000 images from 100 categories with up to keypoints per category. The dataset is split such that categories used for training. validation, and testing do not overlap, and it provides five different category splits to ensure robust generalization. We also report results on AP- 10K (Yu et al., 2021) (supplementary, Section B.2). animal pose dataset covering 23 families and 60 species. Unlike MP-100, it uses a shared keypoint definition and pose-graph structure.
For evaluation, We use a 0.2 threshold for backward compatibility with prior works. But, as this metric became saturated, we follow (Chen et al.) 2025a) and also report mPCK (mean PCK over thresholds 0.05, 0.1, 0.15, 0.2) for more challenging evaluation.
the Probability of Correct Keypoint (PCK) (Yang & Ramanan 2012) metric.
Implementation Details. For a fair comparison, we match the training parameters, data augmen- tations, and data pre-processing to previous works. Additional details in the supplementary (Section CC.5) Notably, our method introduces only minimal latency overhead, adding a lightweight MLPP (Markovian Attention Bias) and single decoder pass (pose-graph predictor), adding only ~22 ms per image (on an A5000 GPU). detailed analysis of computation and complexity is in the supplementary.
4.1 QUALITATIVE RESULTS
Figure provides qualitative comparison between our method, CapeFormer (Si et al. 2023b)
and previous graph-based CAPE work GraphCape (Hirschor & Avidan 2024). CapeFormer does not use any graph prior, while GraphCape uses pre-defined graph prior. With stronger pose-graphs and better mechanism to exploit them via Markov Attention Bias, approach achieves superior localization.
Additionally, Figure provides examples of category-agnostic pose-graph predictions. As can be seen, our network weakens symmetric parts' connections, which can hurt localization. For exam- ple, the table has stronger connections between the base and legs than between the different corners

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Table 1: MP-110 Results. We compare our method against basclines without graph priors or using fixed graphs. Our method consistently outperforms others across all settings and data splits.
Method
Graph Prior
Split
Split
Split
1-Shot
Split
Splic
Avg
Split
Split
Split
5-Shot
Split
Split
CapeFormer
None Fixed
78.44
73.56
73.61
73.14
73.51
74.45
83.71
79.77
79.18
79.62
75.89
79.63
GraphCape
79.87
75.06
76.16
73.58
73.98
75.73
83.93
79.81
78.78
79.02
79.28
80.16
Ours
Predicted
75.68
75.98
77.72
84,92
80.91
Table 2: Point-based Results. PCK@0.2 performance comparison. Our approach outperforms other methods on the 1-shot and 5-shot settings. Best results are bold, second-best are underlined.
I-Shot
S-Shot
Model
Split
Split
Split
Split
Split
Avgg
Split
Split
Split
Split
Split 5
Ang
POMNe1 al 20221
84.23
78. 78.25
78:17
78.65
79.17
79.70
84.72
79.61
73.00
80.38
80.85
80.71
ESCAPE Norvoree eorerr 2174
86.89
82.55
81.25
81.
81.32
82.74
91 .41
87.43
85:33
87.33
86.76
87.63
MesaPoinct
90.43
85 59
84.52
34.34
85.96
86.17
92.58
89.63
89.98
88
39.30
90.02
SD3PNet
91.54
86.72
85.49
85.77
87.26
87.36
93.68
90.23
89.67
89.08
89.46
90.42
85.79
SCAPE
2014
91.47
86.29
87.23
87.07
86.94
87.88 87.80
94.33
90.53
91.49
90.68
80,80
91.37
FMMP
202250
88.19
85 26
83.03
35. 29
84,72
85.30
91.97
89.36
87.35
89.33
88.56
89,31
CapuFormer Shi
2023g
91.07
86
87.05
85. 53
85.77
87.27
94.98
91.47
90.69
90.24
85.62
91.20
GraphCCpp
86
89
90.32
OBI
93.57
89.31
87.38
87.29
89.42
95.45
91.88
01.13
90.28
90.61
91.87
of the base Alternatively, creates new helpful connections in the human face. In the supple- mentary (Section B.3). we demonstrate the instance adaptability of our graph prediction module.
4.2 QUANTITATIVE RESULTS
Following (Chen et al. 2025a), we categorize CAPE methods into two groups: graph and point-based meth- ods. We note that point-based methods largely pursue orthogonal directions and can be combined. Additional metrics and in-depth comparisons are in the supplemen- tary (Section B.
Input Graph
Predicted Graph
Graph-based methods. Table 1 reports mPCK results of graph-based methods on MP-100 under 1-shot and 5- shot settings. CapeFormer serves as a baseline, not us- ing any prior, while GraphCape uses fixed pose-graphs. As shown, our method consistently outperforms other methods across all splits and settings. We achieve sig- nificant gains of +1 +1.99% in 1-shot and +0.9% in 5-shot mPCK over GraphCape. At a finer evaluation threshold (e.g.. 0.05), these margins widen substantially, reach- ing +4.0% and +2.23% respectively. Importantly, the improvements are stable across splits even in cases where GraphCape underperforms CapeFormer (for ex- ample, split 3 in 5-shot), our approach remains superior. This suggests that simply injecting graph priors can be brittle, whereas predicting them yields stronger and more reliable structural prior. Overall, we reach sub- stantial improvement of 3.27% (1 (1-shot) and 1.44% (5- shot) mPCK over CapeFormer.
Figure 5: Predicted Pose-Graphs. We visualize predicted graphs: left column shows input Apric right shows output A. Line width reflects edge weight. Observe the slimmer table-base edges and the new facial edges. Our model prunes symmet- ric part links and forms connections that aid localization.
Posnt-based methods. We show in Table 2 our results compared to recent CAPE models using the more common, but saturated, PCK threshold 0.2. EdgeCape surpasses the previous state-of- the-art, with margins that are on par with and in many cases exceeds the gains reported by prior leading methods. As shown, PCK @0.2 the improvements over GraphCape are +0.92% (1- shot) and +0.37% (5-shot), highlighting that performance using this coarse threshold is approaching saturation. Nonetheless, shot) over SCAPE, the previous non non-graph-ber based ofate-of-the-aae art. Notably, our work can be further combined with other point-based CAPE methods, which we leave for future work.
method achieves impressive gains of +1.62% (1-shot) and +0.5% (5-
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Limitations. Our method consistently enhances accuracy across both low- and higher-shot settings, with particularly notable improvements in the 1-shot scenario. Yet, the performance gain is smaller in higher-shot settings a trend also observed when comparing CapeFormer and GraphCape. This is expected, as graph-based methods are particularly effective for handling occlusions and preserving structure under low-shot conditions. Nonetheless, substantial, as our learned pose-grane pose-graphs and their effective utilization enable consistently stronger gains than those achieved by GraphCape.
contribution in higher-shot settings remains
4.3 ABLATION STUDY
Following standard practice, ablation studies are con- ducted on MP-100's split-1 in the I-shot setting. We analyze the contribution of each component within our framework and examine the impact of input pose-graphs Aprior performance. In the supplementary (Sec- tion B.2). we provide additional extensive ablation exper- iments, including a quantitative comparison in the chal- lenging super cross-category setting, performance using different backbones, evaluation without Apriors evalua- tion of occlusion handling, quantitative analysis of differ- ent design choices and our suggested supervision strategy, and histograms of predicted adjacency weight changes.
Table 3: Method Ablation. We
show mPCK results demonstrating the contribution of category-agnostic pose-graph prediction module and the Markov Attention Bias.
Markov
Attention Bias
(-)
(+)
Pose-Graph Prediction
79.87
80.78
80.76
81.96
Components Contribution. Jshle the importance of both the Markov Attention Bias and the category-agnosti pose-graph prediction model. We build on GraphCape (Hirschorn & Avidan 2024). incorporating each component separately. The results show that both components contribute improved performance, with the greatest gain achieved when they are combined. The Markov Attention Bias not only improves performance on its own but also benefits further from the enhanced predicted pose-graphs, resulting in an even greater overall boost.
The Impact of Prior Graph Input Aprior. To eval- uate the impact of the input gose-graph prior, we com- pare our method to GraphCape using noisy graph in- puts Aprior, as shown Figure 6 We inject up to 16 random edges per pose-graph, comparable to the orig- inal skeleton's 's edge count, spanning noise levels from mild to fully randomized topologies. Since graph-based models rely on connectivity for localization, incorrect connections mislead the model by propagating cues between unrelated keypoints, degrading performance. Yet, our method consistently outperforms GraphCape, with the gap widening as more random edges are added. This highlights our model's s resilience to input pose- graph variations and its ability to predict optimal pose- graphs. Moreover, since the Markov Attention Bias heavily depends on the pose-graph, our superior performance further underscores the effectiveness of our prediction module. This robustness is es- pecially valuable in real-world scenarios where defining an optimal pose-graph is difficult.
Nolsy Ape VS mPCK
Number of Acded tandom Edges
Figure 6: mPCK with noisy Aprior- Test- ing on graphs with randomly added edges shows that our method is robust incor- rect graph inputs, effectively predicting pose-graphs for improved localization.
CONCLUSIONS
We introduce EdgeCape, a CAPE method that improves keypoint localization via predicted category-agnoste pose-graphs. Unlike previous approaches that treat keypoints independently or rely on unweighted user-provided graphs, EdgeCape predicts weighted pose-graphs for more accu- rate localization. By integrating these predictions with Markov Attention Bias, our model captures complex structural dependencies, improving robustness to occlusions and symmetry. On MP-100, 100, EdgeCape achieves ofate-of-the-ar performance in 1-shot and 5-shot settings. Beyond CAPE, our framework highlights the broader potential of weighted graph refinement for category-agnostee vi- sion tasks under data scarcity and structural variability.

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Published as a conference paper at ICLR 2026
Acknowledgment:
Part of this research supported by ISF grant 2132/23.
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A
SUPPLEMENTARY INTRODUCTION -INTRIDUCTTTOOOIIIIIOO
Section B presents additional experimental results for our method, including:
Additional MP-100 quantitative evaluations.
Quantitative evaluations on the AP-10K dataset.
Super-cross category experiment, emphasizing generalization.
Occlusion handling experiment, using query image masking performance comparison.
Supervision strategy ablation studies.
Predicted adjacency matrix change histogram showing the matrices change given different
Aprior inputs, demonstrating the effect of our graph prediction module.
Additional qualitative results including predicted graphs, and qualitative comparisons.
Section C
provides additional details about the different components in our method:
Framework Overview.
Markov Attention Bias.
Training Scheme.
Implementation Details.
B FURTHER EXPERIMENTS
Table 4: MP-100 PCK@0.1 Results. comparison between graph-based methods on the MP-100 dataset. Our method consistently outperforms others across all settings and data splits.
1-Shot
S-Shot
Method
Split
Split 2
Split 3
Split
Split
Avg
Split
Split 2
Split 3
Split
Split
Avg
CapeFormer
74.48
68.39
67.99
68.18
68.86
69.58
81.46
77.12
76.28
76.71
71.86
76.69
GraphCape
76.17
70.05
71.15
68.89
68.95
71.04
81.99
77.22
75.26
75.99
76.28
77.35
Ours
79.29
73.82
73.1
71.16
72.19
73.91
83.32
78.42
76.29
76.82
77.29
78.43
Table 5: MP-100 Additional Metries. AUC and NME performance under 1-shot setting. Our approach outperforms other methods. The best results are bold.
A
NME
Split
Split 2
Split
Split 4
Split
Avg
Split
Split 2
Split
Split
Split 5
Avg
Copeloormer
88.64
86.39
86.18
85.81
86.51
86.70
0.088
0.110
0.111
0.116
0.108
0.106
GraphCape
89.08
87.69
86.97
87.01
86.67
87.48
0.083
0.103
0.104
Ours
90.05
87.95
88.43
87.18
87.40
88.20
0.074
0.094
0.089
0.103
0.103
0.093
B. QUANTITATIVE RESULTS ADDITIONAL METRICS
MP-100 For more complete comparison of our method, we report PCK threshold 0.1 in Ta- ble As shown in Table our method outperforms GraphCape, former SOTA graph-based method by +2.87% on the 1-shot setting and by +1.08% on the 5-shot setting. Overall, we achieve gains of +4.33% and +1.74% over CapeFormer, method that don't use any prior graph structure, em- phasizing the importance of this added refined prior. Notably, even in splits where GraphCape underperforms CapeFormer,
method comes top, underscoring the advantage of graph-based methods over point-based ones when optimal pose-graphs are learned and effectively utilized.
Additionaly include the Area Under ROC Curve (AUC), and Normalized Mean Error (NME) on the MP-100 dataset I-shot setting. As shown in Table 5 our method also excels in these metrics, demonstrating the superiority of our method.
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Ours
92
SCAPE
5-Shat Performance (PCK)
GraphCape
SapeFormer
90
SDPNet
MetaPoint
ESCAPE
POMNet
1 1-Shot Performance (PCK)
Figure 7: MP-100 Comparison. Average PCK@0.2 comparison with recent methods. The circle size indicates the model's 's size. The x-axis and y-axis represent the 1-shot and 5-shot PCK, respec- tively (best top right). Our approach leads both settings.
Point-based methods We also include visual quantitative comparison of the MP-100 IP-100 dataset with recent CAPE methods in Figure 7 It's important to note that most other non graph-based CAPE works are complementary and parallel to ours, meaning that combining different methods could help build even stronger CAPE models.
Computational Complexity. EdgeCape introduces lightweight computational overhead, adding approximately 2.5M parameters and 2 GFLOPs to the GrapCape baseline (totaling R 30 GFLOPs). By leveraging recent transformer optimizations on modern hardware, this translates to modest inference latency increase of only x 2 ms per image on an NVIDIA A5000. Notably, the matrix- power operations required for attention bias are executed using PyTorch's GPU-optimized linear algebra kernels, incurring negligible cost (measured at = 0.02 GFLOPs for 100 keypoints and K 4). While we acknowledge that current CAPE methodologies have not yet reached real-time frame rates, we plan to investigate further efficiency improvements in future work.
B.2 ABLATION STUDY
Table 6: Backbones Ablation. Comparison between graph-based methods on the MP-100 dataset (1-shot, split 1) using different backhones and metrics. Predicted weighted graphs improve perfor- mance, increasing the gap in performance using stronger pre-trained features.
Metric
Method
Backbone
PCK @0.1
mPCK
CapeFormer GraphCape Ours
74.05
77.77
Swin
76.44
79.80
77.66
80.63
CapeFormer
74.48
78.44
GraphCape
DinoV2
76.17
79.87
Ours
79.29
81.96
Performance Using Different Backbones. Table shows results on the MP-100 dataset under
1-shot setting. As be seen, consistently outperform CapeFormer (Shi et al. 2023b) and GraphCape (Hirschorn & Avidan | 2024). highlighting the effectiveness of our approach. Moreover, contrary GraphCape's claim (Hirschorn Avidan 2024), DinoV2 features outperform SwinV2
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in graph-based based CAPE methods. While previous works (Lin al. 2023 Shi al. 2023b Liang
et al. 2024) have found other backbones more suitable for localization tasks, our results suggest that DinoV2' superior generalization and semantic understanding make it especially well-suited for category-agnostic settings. Importantly, we achieve these results while keeping the DínoV2 back- bone frozen, preserving its rich self-supervised representations rather than fine-tuning, done in prior methods. Furthermore, the performance gap increases when predicting pose-graphs using Di- n2 features, aligning with findings by Banani al. (El Banani al. (2024). who demonstrated the intrinsic 3D structural awareness of V2. This structural understanding is particularly beneficial for capturing pose-graph structures.
Table 7: AP-10K Results. PCK@0.1, AUC, and NME results for the single-category AP-10K benchmark.
Metric
Model
PCK@0.1 t
AUC:
NME +
CapeFormer
69.70
86.41
0.094
GraphCape
72.01
87.22
0.086
Ours
72.48
87.29
0.085
Single-Category Setting, We report PCK performance on a single-category pose estimation benchmark using the AP-10K dataset (Yu et al. 2021). large-scale animal pose estimation bench- mark comprising 23 animal families and 60 species. This dataset features a unified keypoint def- inition and skeletal structure across all categories. Notably, the training and testing category splits are not mutually exclusive, meaning some categories may appear in both sets. This setup allows us evaluate our method's effectiveness in single-category context, where categories share common anatomical structures but exhibit species-specific variations.
Results are presented in Table Our method outperforms CapeFormer (Shi et al. 2023b) and GraphCape (Hirschorn & Avidan 2024). showing superiority also in this setting.
Table 8: Super Cross-Category. PCK @0.2 results for the super cross-categon setting. Our method outperforms others in this challenging setting across most splits, demonstrating robust generaliza- tion.
Human Body
Human Face
Method
Vehicle
Furniture
POMNet (u et al.
2022a
73.82
79.63
34,92
47.27
ESCAPE Nguyen al. 2024
80.60
84.13
41.39
55.49
MetaPoint+ CChnn et al. 2024
84.32
82.21
46,51
53.67
SDPNet Ren
2024
83.84
81.24
45.53
53.08
SCAPE (Tang al
84.24
85.98
45.61
54.13
CapeFormer (Shi al.
2023a
83.44
80.96
45.40
52.49
GraphCape (Hirschorn & Avidan 2024
88.38
83.28
44.06
45.56
Ours
91.38
87.61
44.12
54.03
Super Cross-Category We conduct srost-uuperccategory experiment following prior works evaluate our model's generalization capacity. Splitting the MP-100 dataset into eight super- categories, we hold out each super-category in turn and train on the remaining categories. As shown in Table our model outperforms other graph- based methods across most splits. Notably, in the furniture split, GraphCape (Hirschorn & Avidan 2024) performs worse than CapeFormer (Shi et al. 2023b), indicating that in this case, the input pose- pose-graph impedes performance. However, our graph-prediction network not only surpasses GraphCape but also outperforms CapeFormer. This demonstrates that our model successfully learns pose-gree graphs that improve localization, even when the input pose-graphs suboptimal.
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Performance Without Prior Knowledge To assess the model's dependency on informative struc- tural priors, we conducted an ablation test using fully-connected graphs, which essentially removes topology-basee -aaegy information. Under this setting GraphCape suffer performance collapse, revert- ing to the baseline CapeFormer level Hirschorn & Avidan (2024). In contrast, EdgeCape demon- strates significant robustness, achieving +1 1.29% mPCK improvement uninformative fully-connected graphs. Notably, this performance is on par with what GraphCape achieves using its standard input prior structure. This confirms that our method provides distinct benefits even when the graph prior carries no useful information.
CapeFormer even with
Adjacency Matrix Supervision. To evaluate the effectiveness of unsupervised masking strat- egy, we first test the performance using the GCN reconstruction approach proposed by Ren et al. (Ren al. 2024). which utilizes an auxiliary GCN to reconstruct masked inputs. With this approach, achieve (PCK @0.2) 92.88 % accuracy, compared to 93.57% % using our decoder- based reconstruc- tion strategy a decrease of 0.69%. This drop supports our hypothesis that structural information embedded within the auxiliary GCN weights and thus not retained during inference.
Furthermore, we investigate the impact of our decoder reconstruction strategy under varying levels of input keypoint masking. The results of this analysis are illustrated in Figure As shown, masking keypoints enhances the supervision signal for the adjacency matrix, leading to more robust and structurally meaningful graph representations.
Keypoint Masking Supervision vs PCK
nOk
20.5
15.1
Masking Percentage
%
Figure 8: Keypoint Masking Supervision. Effect of the proposed unsupervised masking strategy with varying percentages of masked input keypoints
Grse-Graph Predictor Design. We also ablate different design choices for the pose-graph pre- dictor. First, we check the contribution of using full-image features compared to just the keypoint features to predict pose graphs. As hypothesized, using only keypoint features results in a decrease of 0.24% % compared to using all features. This validates that the full-image features' global context helps infer structural relations more effectively.
Moreover, ablate the strategy of predicting pose graphs using the refined features. We compare the following strategies:
Using the suggested cosine similarity:
AAi =< F Fieeeeed F F refined
>
(11)
Using an MLP-based prediction:
Aii = MLP ( || Fr Feetindd
(12)
Using multi- attention-based prediction:
AAg = Linear ([oi KK ... || Qir kx
(13)
All methods achieve similar performance. Thus, we opt for the simplest design and use cosine similarity as our preferred strategy.
Handling Occlusions. To demonstrate the effect of weighted graph information in handling oc- clusions, applied random partial masking to the query images before executing our algorithm. As shown in Figure D our method consistently surpasses others when parts the query images are masked, accurately predicting keypoints.
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Query Image Masking PCK
Ma MaskQurry Ours
Query GraphCape
Mask Query CapsForm
Masking Percentage
Figure 9: Handling Occlusions. P.2@0.2 results when masking the query image. Our method consistently surpasses GraphCape, leveraging cues provided by the weighted graph structure to over- come information gaps in the query images.
(a) Annotated Graph Prior
(b) Self-loops Prior
(c) Fully-Connected Prior
Figure 10: Adjacency Matrix Change Histogram. A histogram showing the weight changes of our predicted weighted graph. We show the change given different Aprior imputs. the x-axis shows the change and the y-axis is the log- frequency. The dashed red line shows change in edge weight.
Adjacency Matrix Change. We assess how the weights of the adjacency matrices change. We create a histogram of the difference between normalized Aprior and the normalized predicted A matrix. Results are shown in Figure [O] demonstrate that our network effectively refines the input graph prior. This refinement involves both creating new connections and adjusting the weights of existing ones, enabling the model to better represent structural relationships.
B.3 ADDITIONAL QUALITATIVE RESULTS
Figure [T shows the instance adaptability of our graph prediction module. As can seen, our graph prediction predicts different graphs for instances of the same category given different inputs. Moreover, Figure [2] shows skeleton predictions for different input values of Aprior We use self- loops, visualizing performance without any prior knowledge, and with random omission graphs, where we drop some of the edge connections randomly. In addition, Figure 13 illustraasss additional skeleton predictions.
We also present in Figure 14 additional qualitative comparison for Capeformer Shi et al. 2023a
GraphCape (Hirschomn & Avidan 2024), and the non graph- based SCAPE (Liang al. 2024 on various categories.
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Input Graph
Predicted Graph
Zoom In
14
15
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16
13
11
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Figure 11: Instance Adaptability. We visualize an example of instance adaptability. The left column denotes the input Aprior and the right column is the refined adjacency matrix. In the top row, we see graph input of panda body, where all keypoints are visible. In the bottom row, as some keypoints are occluded (node 15), the input graph includes isolated nodes (node 16). Our predicted graph connects this isolated node to enhance localization.
Input Graph
Predicted Graph
Input Graph
Predicted Graph
Figure 12: Predicted Graphs with Various Aprior. We visualize the unnormalized graph outputs with various Aprior inputs. The left column denotes the input Aprior and the right column the refined adjacency matrix. Line width corresponds to edge weight.
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Input Graph
Predicted Graph
Input Graph
Predicted Graph
PM
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Figure 13: Predicted Skeleton. We visualize the unnormalized graph outputs. The left column de- notes the input Aprior and the right column is the refined adjacency matrix. Line width corresponds to edge weight. The model disconnects symmetric parts and creates new connections that are helpful for localization.
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Support
GT
CapePromer
SCAPE
GraphCape
EdgeCape
FIIL
Figure 14: Qualitative Comparison. We visualize keypoints predictions for the 1-shot setting. The left column denotes the support image with its corresponding skeleton. The second column is the ground-truth query keypoints. The following columns are results from CapeFormer, SCAPE, GraphCape, and our method.
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C METHOD DETAILS
C.I FRAMEWORK OVERVIEW
We base our method on GraphCape (Hirschorn Avidan 2024). introducing two main architectural
modifications:
Incorporating Markov Bias attention in the graph-decoder.
Adding a category-agnostic pose-graph prediction module.
These modifications are detailed in the main paper, while below we provide brief overview of GraphCape's architecture for completeness. For full details, please refer to the original paper.
Feature Extractor: A pre-trained model extracts features from both support and query images. Support keypoint features are derived by element-win wise multppiccaiion between the support image's 's feature map and keypoint masks, created using Gaussian kemels centered at the support keypoints. In multi-shot scenarios (e.g.. 5-shot), the average of support keypoint features across images in feature space is taken. This results in the query feature map Fg E hwwxC and support keypoint features F. E RxC
Transformer Encoder:
The transformer encoder fuses information between support key- point and query patch features. It comprises three transformer blocks, each with a self- attention layer. Support keypoints and query features are concatenated before entering the self-attention layer and separated afterward. The output is the refined query feature map Fg and refined support keypoint features Fs.
Similarity-Aware Proposal Generator: GraphCape builds CapeFormer's two-stage approach, first generating initial keypoint predictions, which are then refined via DETR- based transformer decoder. The proposal generator aligns support keypoint features with query features, producing similarity maps from which peaks are selected as similarity- aware proposals. To enhance efficiency and adaptability, a trainable inner-product mecha- nism (Shi et al.) 2022) is used to explicitly model similarity.
Graph Transformer Decoder: A transformer decoder network decodes keypoint loca- tions from the query feature map. It contains three layers, each with self-attention, cross- attention, and feed-forward blocks. In this design, GraphCape replaces the simple MLP in the transformer decoder's ferwaforward network with a GCN-based module incorpo- rate structural priors directly into the keypoint prediction process. To prevent excessive smoothing- a common issue in deep GCNs that can blur node distinctions and degrade performance-Graphee adds linear layer for each node following the GCN layer:
F Winnear (Wadff Fh Aprior + Weelf F
(14)
Where W; are learnable parameters, Juct is an activation function (ReLU), and Aprior
k is the symmetrically normalized adjacency matrix.
Using an iterative refinement strategy (Cai & Vasconcelos) 2018 Teed Deng 2020 Zhu
et al., 2020), each decoder layer predicts coordinate deltas for prior predictions. that the updated coordinates are created as follows:
This means
pi+ pi+1= = o + MLP((F!+1))
(15)
where a and o- are the sigmoid and its inverse. Additionally, the decoder leverages pre- dicted coordinates provide enhanced reference points for feature pooling from the image feature map. The keypoints' positions from the last layer are used as the final prediction.
C.2 CATEGORY-AGNOSTE POSE- GRSPH PREDICTION
The architecture of our pose-graph prediction network fo is illustrated in Figure 15 Formally, we define a learnable function Jo to learn the residual pose-graph in the form of an adjacency matrix AA E k
AA So (Arrorr Ps, F*)
(16)
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Arrior
F
F
Self Attention
Cross Attention
GCN
Cross Attention
Cosine Similarity
AA
Figure 15: Category-Agnostic Pose-Graph Predictor. Our pose-graph predictor (as referenced in Figure 3 uses the prior input graph Aprior, the support image features F, and keypoint features F to produce structure-aware keypoint features Ek reffnedd Cosine-similarity is then applied to predict the residual adjacency output AA.
where Aprior (0, 10, xxK is the unweighted graph input provided by the user (like in GraphCape), and F, kox and F IR. KxC are the support image and keypoint features.
To handle the few-shot setting, our pose- gase-graph predictor refines the features of each support image independently, allowing the model to account for structure on a per-image basis. After refinement, we average the resulting structure features across all support images and predict unified adjacency matrix. This aggregation enables the method to exploit structural cues from all available support instances, ensuring that the final structure estimate benefits from much visible information as possible. Note that the prior skeletal relations (Aprior) are category-specific and therefore identical across all support images.
C.3 MARKOV ATTENTION BIAS
In category-agnostic pose estimation, encoding spatial relations between keypoints is beneficial for robust localization, especially when dealing with novel objects (Hirschorn & Avidan 2024 Ru- sanovsky et al. 2024). Transformer models naturally have a global receptive field, allowing each node (or token) to attend to all others within a layer. However, this flexibility introduces a challenge: Transformers lack inherent structural constraints, so positional dependencies must be explicitly en- coded to reflect local relations. While this often done in sequence data through absolute or relative positional encodings, graphs pose different challenge, nodes not arranged linearly and con- nectivity is defined by edges.
Our approach aims to better utilize the structural dependencies between keypoints for CAPE. We build on the foundations set by GraphCape (Hirschorn Avidan 2024). which incorporates GCN layers into the feed-forward layers to propagate structural information. However, the GCN layers used that model were limited by their fixed, nearest- neerestt receptive field, which restricts the model's ability capture more complex distant connections between keypoints.
Thus, we further integrate the graph-prior into the architecture. We follow Ying et al. (iin et al., 2021). adding bias term based on graph connectivity the self-attention mechanism in the decoder. Denote azj as the (i. -elemet of the Query-Key product attention matrix resulting in:
O.j (h, (h, Wx)
)o(())y)
(17)
Vd
where bo(w).y)) is a learnable scalar indexed by o(u,, and is unique for each attention head. Unlike the limited receptive field in GCNs, using bo(v.),) enables each node in single Transformer
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X,Y
F
Fr
Marcvvinn Atarrtin
Crowe Alterton
DCN
AY
Figure 16: Graph Decoder Prediction Layer. Overview of the Transformer decoder architecture, adapted from the GraphCape design. The decoder consists of self-atiention, cross-attention, and graph-based feed-forward network. We incorporate Markov Attention Bias into the self-attention mechanism encourage structural keypoint interactions. Self-attention facilitates adaptive inter- actions among support keypoints, while cross-attention extracts localization information from the input features. Finally, the decoder refines keypoint features and outputs location predictions.
layer to adaptively attend to all other nodes based on the graph's structural information. 6(0;, uy) is usually the distance of the shortest path (SPD) between v. and 1y if the two nodes are connected.
This bias term is highly effective in capturing general structure, boosting the performance of Graph- Cape by around 0.5%. However, assumes discrete distance between nodes and is applicable for unweighted adjacency matrices. Our primary objective is to predict real-valued edges, representing the strength of the structural keypoints connections. Thus, inspired by Ma et al. (M. et al. 2023a) we treat the normalized adjacency matrix as a right stochastic matrix.
A stochastic matrix is commonly used describe the transitions in Markov chain, where each element (A),, represents the probability moving from one state (or node) 2 another state uy in single step. Specifically, the entry (A*);y gives the probability of transitioning from node v to node vy exactly k-hops. This process allows capture both direct and indirect relations between keypoints, enabling the model to consider more distant keypoints that may influence localization. Thus, we build the following matrix:
P.j = -,,, -,=, ER
(18)
where I is the identity matrix and the parameter K controls the maximum number of hops consid- ered. We incorporate this graph characteristic into our model's 's attention mechanism to enable more nuanced and flexible structural priors. The complete decoder layer is illustrated in Figure [6 we use the following bias term:
Based
on Equations 17 and T8
0ij (h, Wx)" + MLP(P.;)
(19)
Vd
where MLP: R + R. modulates the influence of keypoints based on their distance in the graph (i.e., the number of hops between them). This formulation results continuous and learnable structure-based bias term.
C.4 TRAINING SCHEME
When we attempt to directly integrate the adjacency matrix predictor and the self-attention bias mechanism, observe only small impact the model's performance. Changing the adjacency
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matrix while training the bias attention MLP results in unstable training. Thus, first train base model and fine-tune each added component.
We begin by training the base model and establishing strong foundational features necessary for robust localization. Optimization is done using foff which is the L1 localization loss as in (Shi integrate the skeleton predictor. This stage allows
al. 2023b). Once the model has converged, the model to further adapt by incorporating specifie structural insights provided by the skeleton predictor. For this phase, add the adjacency loss, resulting in:
L Losfect + Audj Ludj+
(20)
In the final phase, we maintain the frozen feature extractor and freeze the skeleton predictor. Then, we integrate the Markov bias attention. This final stage allows the model strengthen its capacity to interpret spatial dependencies between keypoints.
This three-phase approach allows each component integrate structural encoding progressively, enhancing accuracy through stable framework.
C.5 IMPLEMENTATION DETAILS
For a fair comparison, training parameters, data augmentations, and data pre- pre-prceessing are kept the same in previous works. In addition, the backbone size matched to other works, thus, the smallest (~20M parameters) versions of V2in and DinoV2.
The graph predicting network, encoder, and keypoint prediction decoder have 3 layers. For the Markov Bias Attention, we use maximum of K = 4 hops, and for Ladj we mask 50% of keypoints, using Andj 1. The model is built upon MMPose framework (Contributors, 2020), trained using Adam optimizer with batch size of 16, the learning rate is 10 and decays by 10x the 160th and 180th epoch. Each phase is trained for 100 epochs. Training takes ~ hours on a single Nvidia A100 GPU.
Evaluation of SCAPE (Liang et al. 2024) and FMMP (Chen et al. 2025a) was done by removing the keypoint identifiers (which shown be inapplicable for real-world scenarios (Nguyen al.) 2024 Yang et al. 2024 Hirschorn & Avidan (024g) and training a network using their official code. As FMMP leverages a larger number of keypoints, experienced relatively greater performance drop without these identifiers. For SCAPE, we use DinoV2 as the backbone to ensure a fair compar- ison. And, since FMMP relies on multi-scale features, we adopt modern pre-trained multi-scall vision transformer backbone (PVT-V2). We also used GraphCape and CapeFormer official code for evaluation and visualizations.
D THE USE OF LARGE LANGUAGE MODELS (LLMS)
This article was refined with the help of LLMs to improve clarity and style. They helped polish the writing.
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Reviews:
[{"review_id": "p3cElrPazn", "reviewer": "Reviewer_zWUR", "summary": "The paper introduces EdgeCape, a spectral pre-conditioning strategy for GNNs that rescales edge weights before message passing to improve numerical stability and convergence. The idea is simple and general, and it can be plugged into standard GNN layers. Experiments on common benchmarks show small but consistent accuracy gains.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": 2, "contribution": 2, "strengths": "The motivation is reasonable and relevant. \n\nThe approach is lightweight, easy to integrate, and empirically helps across several architectures. \n\nThe writing is clear, and the empirical section is well-organized with ablations.", "weaknesses": "- The conceptual novelty is limited: many prior works already consider spectral normalization, Laplacian smoothing control, or adaptive edge reweighting. EdgeCape largely reformulates these ideas as “pre-conditioning” without delivering substantial theoretical or algorithmic innovation.\n\n- The theory is weak, providing heuristic spectral bounds but no solid convergence or generalization proof.\n\n- The experimental gains are modest (1–2%), limited to small and medium datasets, and lack comparison to stronger modern baselines in 2025.\n\n- The scalability and robustness on large or dynamic graphs are untested.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DJD9FoooKW", "reviewer": "Reviewer_SPW6", "summary": "The submission focused on the task of category-agnostic pose estimation with one or few annotated support images. Specifically, the authors propose a novel framework named EdgeCape to predict the graph's wdge weights for optimal localization. The authors also employ Markov Attention Bias to modulate the self-attention interaction in multi-hop. The experiments are conducted on a large-scale benchmark datasets, which indicate the effectiveness of the proposed method.", "questions": "See Weakness.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The task of category-agnostic pose estimation is interesting and fundmental for extending the category number of pose estimation.\n\n2. The idea of using graph-based model is reasonable and makes sense.\n\n3. The proposed EdgeCape and Markov Attention Bias are novel and effective to model the structral information of objects.\n\n4. The performances of proposed method are shown on large-scale benchmark dataset, and outperform baselines by a large margin.\n\n5. The experimental analyses are extensive and in-depth.", "weaknesses": "1. How to deal with the edge missing issue if there are some keypoints being occluded? E.g., the same objects may have different graphs if the occlusion cases are different. More specifically, the query image has 3 occluded keypoints, while the support image has another 5 occluded keypoints.\n\n2. What's the complexity of proposed method? It seems to be about O(K^2). Is it cost-effective?\n\n3. Closely related works are missing in the related works.&#x20;\n\n   > 1. @inproceedings{chen2025weakshot, title={Weak-shot Keypoint Estimation via Keyness and Correspondence Transfer}, author={Chen, Junjie and Luo, Zeyu and Liu, Zezheng and Jiang, Wenhui and Li, Niu and Fang, Yuming}, booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}, year={2025} }\n   >\n   > 2. @inproceedings{kim2025capellm,   title={CapeLLM: Support-Free Category-Agnostic Pose Estimation with Multimodal Large Language Models},   author={Kim, Junho and Chung, Hyungjin and Kim, Byung-Hoon},   booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, year={2025} }", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9zObWQgYM9", "reviewer": "Reviewer_qcXW", "summary": "This paper presents a one-shot, category-agnostic method for estimating joint positions and their topological relationships. The approach begins with a user-provided binary skeleton and predicts real-valued edge weights tailored to the test instance, rather than assuming uniform importance across links. The authors introduce a Markov Attention Bias that interprets the weighted adjacency as a stochastic matrix to inject multi-hop connectivity cues directly into Transformer self-attention. Experiments on the MP-100 dataset show state-of-the-art performance compared with strong baselines.", "questions": "Please respond to the weakness part.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) The paper presents a novel architecture that transitions from fixed, unweighted pose graphs to data-driven, weighted graphs.\n\n(2) The proposed graph-based model incorporates a Markov Attention Bias to better capture complex spatial dependencies among keypoints.\n\n(3) The writing is generally clear, and the figures are well designed.", "weaknesses": "(1) Marginal performance improvement over baselines on the MP-100 dataset, especially in the 5-shot setting: in Table 1 and 2, the performance gain over the strongest baseline is less than 1%.\n\n(2) Missing references and comparisons (Minor). The paper lacks comparisons with related works, such as AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cvIyafUDcl", "reviewer": "Reviewer_HSXw", "summary": "This paper presents EdgeCape, which addresses the issue of fixed edge weights in graph structures for Category-Agnostic Pose Estimation (CAPE). The authors introduce an edge weight prediction module combined with a \"Markov Attention Bias\" mechanism, claiming to achieve SOTA results on MP-100.", "questions": "The authors casually mention *only ~2ms additional delay*. However, the Pose-Graph Predictor requires an extra decoder pass and matrix power computations ($Ã^k$). With $K=4$ and large keypoint counts, which is far from negligible in practical deployment. The authors must provide detailed FLOPs or parameter count comparisons.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The problem identification is precise. This is the first work to systematically identify the limitations of fixed unweighted pose-graphs in CAPE and propose \"category-agnostic edge weight prediction\" as a novel and practical research direction.\n\n2. The proposed Pose-Graph Predictor effectively fuses global image context with keypoint features, using residual graph optimization to avoid learning structures from scratch while balancing stability and expressiveness. The Markov Attention Bias cleverly injects graph structural information into Transformer attention mechanisms, enhancing spatial relationship modeling.\n\n3. Not only achieving SOTA on the standard MP-100 benchmark, but also validating method effectiveness through multi-dimensional tests including noisy graph robustness (Figure 6), cross-supercategory generalization (Table 8), and occlusion experiments (Figure 9). The results particularly demonstrate that EdgeCape significantly outperforms GraphCape even when A_prior is suboptimal, and even surpasses graph-free methods like CapeFormer.", "weaknesses": "1. Unfair Comparison with CapeFormer Due to Asymmetric Use of Structural Priors\nThe paper claims superiority over CapeFormer, a point-based CAPE method that does not use any pose-graph prior. However, EdgeCape relies on a user-provided $A_{prior}$ as input to its graph refinement module. While the authors justify this by stating that $A_{prior}$ is part of the *support data* in graph-based CAPE, the comparison remains misleading unless the robustness of this prior is rigorously tested. Crucially, Figure 6 shows that EdgeCape significantly outperforms GraphCape under noisy $A_{prior}$, suggesting that the method's strength lies not just in using a graph, but in correcting a potentially bad one.Therefore, to make the comparison with CapeFormer meaningful, the authors should evaluate EdgeCape under a degraded or randomized $A_{prior}$ (e.g., fully connected, self-loops only, or random edges), a setting where the prior provides little to no useful signal. If EdgeCape still outperforms CapeFormer in such a scenario, the claim of superiority would be far more convincing. As it stands, the reported gains may largely stem from access to privileged structural information rather than algorithmic innovation.\n\n2. The paper shows successful cases (Figures 4, 5) but provides no examples where EdgeCape fails while GraphCape/CapeFormer succeeds. This raises concerns about robustness. For instance, on highly symmetric or severely occluded objects, might the predicted weighted graph introduce erroneous strong connections that mislead localization?\n\n3. The ablation study (Table 3) is misleading. It only compares the presence/absence of two components. Is the predicted weighted graph $Ã$ actually better than the original $A_{prior}$? The authors should fix the decoder and compare performance using $A_{prior}$ versus $Ã$ as inputs. Otherwise, performance gains might entirely stem from Markov Bias or training strategies rather than the graph itself.\n\n4. The novelty of the proposed method is limited. The core idea of EdgeCape (predicting edge weights and introducing graph structure into attention mechanisms) lacks substantial breakthroughs at the methodological level. The weighted graph prediction uses standard cosine similarity, and the Markov Attention Bias is just a re-packaging of multi-hop structure encoding, which belongs to engineering improvements rather than principled innovations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)