Generative Zero-Shot Learning (GZSL) methods address the challenge of recognizing unseen classes by synthesizing visual features, thereby converting ZSL into a supervised learning task. However, existing approaches are predominantly constrained to two multi-stage strategies: pre-generation prior knowledge enhancement and post-generation feature refinement. These paradigms often suffer from error propagation across stages, ultimately limiting generation quality and representational fidelity. To overcome these limitations, we propose DKGZSL, a novel generative framework that injects dynamic visual-semantic knowledge directly into the feature synthesis process, effectively unifying generation and refinement into a single cohesive stage. Specifically, a Knowledge Transfer Network (KTN) is introduced to convert semantic information into hierarchical visual knowledge representations. To ensure accurate semanticvisual alignment, we further design a Semantic-Oriented Visual Refinement (SOVR) module that reshapes real visual features into semantically aligned and noise-suppressed representations, providing precise guidance for the KTN. Moreover, hierarchical knowledge extracted from each KTN layer is progressively transmitted to the generator via Meta-Fusion Units (MFUs), enabling dynamic semantic guidance and improving generation quality. Extensive experiments on three benchmark datasets demonstrate that DKGZSL achieves consistent state-of-the-art performance with both ResNet-101 and ViT-B/16 feature extractors. Comprehensive ablation studies further confirm the effectiveness and complementarity of each proposed component. The code is available at https://github.com/JingHu-gdut/DKGZSL.

Zero-shot anomaly detection (ZSAD) aims to identify and localize anomalies in previously unseen target domains without accessing any target-domain training data, which is crucial under privacy, security, or proprietary constraints. However, existing ZSAD methods often struggle to generalize across domains, as they are tightly coupled to specific object categories or rely on fragmented designs that fail to capture both semantic consistency and structural abnormality. In this paper, we propose UAD, a unified framework that addresses ZSAD from a holistic perspective by jointly modeling semantic regularity and anomaly-aware representations. The key insight of UAD is that effective ZSAD requires aligning multi-level semantic understanding with fine-grained structural cues, rather than relying solely on object-centric semantics or local appearance statistics. To this end, UAD organizes image representations into coherent semantic contexts and identifies anomalies as deviations from both local structural patterns and high-level semantic consistency. Furthermore, we enhance cross-domain robustness by improving semantic supervision and diversity through prompt concatenation and intensity-guided anomaly synthesis, enabling UAD to better generalize to unseen anomaly types and domains. Extensive experiments on 17 real-world anomaly detection datasets show that UAD achieves superior zero-shot performance of detecting and segmenting anomalies in datasets of highly diverse class semantics from various defect inspection and medical imaging domains. Our works are available at https://github.com/hanli6688/UAD.

Universal domain adaptation seeks to extend knowledge from a labeled source domain to an unlabeled target domain, unconstrained by label space alignment. It is challenging to align shared categories and separate private ones without prior category overlap information. Existing methods often rely heavily on source domain information to learn a transferable classifier, neglecting the relationships between the manifold structures inherent within the two domains. This paper introduces a novel framework, called Guiding cOntrastive Learning with out-of-distribution Detection (GOLD) for universal domain adaptation. GOLD employs an instance-prototype hybrid contrastive learning approach with self-attention to reveal domain structures. Subsequently, it constructs a residual subspace from source prototypes to filter unknown categories and refines neighborhood structures through instance-level virtual adversarial training to reduce noise. Experiments on three datasets show GOLD surpasses current methods in various UniDA settings.

Sketch-based 3D shape retrieval has attracted increasing attention in recent years. Most existing methods fail to address the zero-shot scenario, and the few dedicated to zero-shot learning encounter the following two issues: 1) the features learned by these methods lack informativeness and generalization, rendering them ineffective in identifying unseen samples; 2) the generation of low-quality samples, aimed at facilitating the recognition of unseen categories, paradoxically diminishes their ability to identify these unseen classes. This paper introduces a novel contrastive disentanglement generative adversarial networks (CoDi) tailored for zero-shot sketch-based 3D shape retrieval. Initially, we introduce a paradoxical feature construction approach designed to assist the networks in capturing certain low-level features. Despite their weak semantic relevance, these features play a crucial role in sample recognition. Subsequently, a SemContrast fusion module is employed to align the semantic space with the prototype embedding space of categories. This alignment facilitates knowledge transfer to unseen classes and promotes the generation of high-quality samples. The networks are jointly trained on real and generated samples to achieve retrieval for unseen categories. Extensive experiments demonstrate a significant improvement in retrieval performance for unseen categories using our method.

Cross-modal hashing has gained considerable attention in cross-modal retrieval due to its low storage cost and prominent computational efficiency. However, preserving more semantic information in the compact hash codes to bridge the modality gap still remains challenging. Most existing methods unconsciously neglect the influence of modality-private information on semantic embedding discrimination, leading to unsatisfactory retrieval performance. In this paper, we propose a novel deep cross-modal hashing method, called Semantic Disentanglement Adversarial Hashing (SDAH), to tackle these challenges for cross-modal retrieval. Specifically, SDAH is designed to decouple the original features of each modality into modality-common features with semantic information and modality-private features with disturbing information. After the preliminary decoupling, the modality-private features are shuffled and treated as positive interactions to enhance the learning of modality-common features, which can significantly boost the discriminative and robustness of semantic embeddings. Moreover, the variational information bottleneck is introduced in the hash feature learning process, which can avoid the loss of a large amount of semantic information caused by the high-dimensional feature compression. Finally, the discriminative and compact hash codes can be computed directly from the hash features. A large number of comparative and ablation experiments show that SDAH achieves superior performance than other state-ofthe- art methods.

Domain adaptation has shown appealing performance by leveraging knowledge from a source domain with rich annotations. However, for a specific target task, it is cumbersome to collect related and high-quality source domains. In real-world scenarios, large-scale datasets corrupted with noisy labels are easy to collect, stimulating a great demand for automatic recognition in a generalized setting, i.e., weakly-supervised partial domain adaptation (WS-PDA), which transfers a classifier from a large source domain with noises in labels to a small unlabeled target domain. As such, the key issues of WS-PDA are: 1) how to sufficiently discover the knowledge from the noisy labeled source domain and the unlabeled target domain, and 2) how to successfully adapt the knowledge across domains. In this paper, we propose a simple yet effective domain adaptation approach, termed as self-paced transfer classifier learning (SP-TCL), to address the above issues, which could be regarded as a well-performing baseline for several generalized domain adaptation tasks. The proposed model is established upon the self-paced learning scheme, seeking a preferable classifier for the target domain. Specifically, SP-TCL learns to discover faithful knowledge via a carefully designed prudent loss function and simultaneously adapts the learned knowledge to the target domain by iteratively excluding source examples from training under the self-paced fashion. Extensive evaluations on several benchmark datasets demonstrate that SP-TCL significantly outperforms state-of-the-art approaches on several generalized domain adaptation tasks. Code is available at https://github.com/mc-lan/SP-TCL.

Unsupervised domain adaptation is an appealing technique to learn robust classifiers for unlabeled target domain by borrowing knowledge from well-established source domain. However, previous works mainly suffer from two limitations: 1) the classifier trained on labeled source data may be prone to overfitting the source distribution, lowering its performance on the target domain; 2) the adaptation process will be misled by conditional distribution matching using hard pseudo labels of target samples. This paper presents a Dual-Level Adaptive and Discriminative (DLAD) classifier learning framework, in which transfer classifier and distribution adaptation can be mutually beneficial for effective knowledge transfer. Specifically, we aim to achieve a domain-level adaptive classifier by considering structural risk minimization (SRM) on both domains and performing weighted distribution adaptation, which facilitates joint classifier learning in a semi-supervised manner. To further achieve a class-level discriminative classifier, we explicitly leverage unlabeled target data to promote classifier learning based on class probabilities, which refines the decision boundary to be more discriminative for unlabeled target data. To the best of our knowledge, DLAD is the first attempt to consider the principle of SRM on the target domain, which significantly boosts the discriminative power of transfer classifier and yields a tighter generalization bound. Experimental evaluations on several standard cross-domain datasets show that DLAD significantly outperforms other competitive methods.

Cross-modal retrieval has attracted widespread attention in many cross-media similarity search applications, especially image-text retrieval in the fields of computer vision and natural language processing. Recently, visual and semantic embedding (VSE) learning has shown promising improvements on image-text retrieval tasks. Most existing VSE models employ two unrelated encoders to extract features, then use complex methods to contextualize and aggregate those features into holistic embeddings. Despite recent advances, existing approaches still suffer from two limitations: 1) without considering intermediate interaction and adequate alignment between different modalities, these models cannot guarantee the discriminative ability of representations; 2) existing feature aggregators are susceptible to certain noisy regions, which may lead to unreasonable pooling coefficients and affect the quality of the final aggregated features. To address these challenges, we propose a novel cross-modal retrieval model containing a well-designed alignment module and a novel multimodal fusion encoder, which aims to learn adequate alignment and interaction on aggregated features for effectively bridging the modality gap. Experiments on Microsoft COCO and Flickr30k datasets demonstrates the superiority of our model over the state-of-the-art methods.

Cross-view consistency is the fundamental property of multiview clustering. However, in incomplete multi-view scenarios, existing methods can only pursue consistency through the paired data while ignoring the information in unpaired data. In this paper, we show a new insight from the data pattern and provide a novel perspective to incorporate unpaired data for consistency maximization by mining group correspondence. We first formulate cross-view consistency in a statistical perspective to by-pass the strict demand of instance correspondence, and then propose a technique to construct corresponding groups across views to enhance the objective of consistency maximization. Our proposal can be used as a universal plug-in to augment existing approaches. We test the efficacy and generality of our proposal by adapting it to two base methods as augmentations and comparing the augmented models against the original ones and other baselines. Experiment results demonstrate the effectiveness of our proposal and validate the value of our insight.

Most of the existing cross-modal hashing (CMH) methods are trained in a batch-based manner, which is time-consuming and unable to handle streaming data. Recently, online CMH methods have attracted increasing attention. However, existing online CMH methods face two limitations: 1) they usu-ally excavate the common semantic information by learning modality-specific projection matrix for each modality, while ignoring the intrinsic relationship among different modali-ties; 2) they often suffer from learning less discriminative hash codes because of insufficiently exploiting the pairwise similarity. To tackle these challenges, we propose a novel Online Robust Specific and Consistent Hashing (ORSCH) method. Specifically, ORSCH decomposes the projection ma-trices into consistent and modality-specific ones, which ef-fectively exploits intrinsic semantic information of streaming data in different modalities. Furthermore, we utilize both dis-crete and continuous labels to construct affinity matrices to improve the discrimination of hash codes. Experiments on three benchmark datasets show the superiority of ORSCH.

Most existing unsupervised domain adaptation methods learn domain-invariant representations with entangled domain in-formation, semantic information, and instance information. Differently, in this paper, we propose a Triple Disentangling Network (TDN), to disentangle these three types of information and then predict the target labels merely using semantic information. Specifically, TDN consists of a reconstruction module and a disentanglement module. In the reconstruction module, TDN utilizes a variational auto-encoder to re-construct the domain, semantic, and instance latent variables behind the data. In the disentanglement module, adversar-ial learning, discriminative clustering, and instance separation are seamlessly integrated to disentangle these three sets of re-constructed latent variables. Significantly, TDN can not only effectively alleviate the negative transfer of outliers through disentangling instance information, but also disentangle se-mantic information more thoroughly by exploring discriminative structure knowledge. Experimental studies on two bench-mark datasets demonstrate the superiority of TDN.

Abstract Microaneurysm is a kind of small targets in color retinal image, and it is an essential work to recognize the small target for the early diagnosis of diabetic retinopathy. This paper proposes an efficient method to accurately recognize microaneurysm. A symmetric extended curvature Gabor wavelet is presented to generate candidate objects, where some novel features are extracted for classification. A kind of statistic features is generated to distinguish between microaneurysm and thin vessels, in terms of the shape similarity of cross-section profiles. Furthermore, the visual attention-based features are proposed to compute local contrast of small targets in complex background. Random undersampling with AdaBoost (RUSBoost) classifier is employed to discriminate true microaneurysm from an overwhelming amount of candidate objects. Experimental results demonstrate that the proposed method achieves significant sensitivity and accuracy on the public datasets, in comparison to the state-of-the-arts.

In real-world applications, complete or incomplete multi-view data are common, which leads to the problem of generalized multi-view clustering. Recently, researchers attempt to learn the latent representation in the common subspace from heterogeneous data, which usually suffers from feature degeneration. Moreover, there are limited efforts on simultaneously revealing the underlying subspace structure and exploring the complementary information from incomplete multiple views. In this paper, we introduce a novel Generalized Multi-view Collaborative Subspace Clustering (GMCSC) framework to address the above issues, in which consensus subspace structure of all views and embedding subspaces for each view are jointly learned to benefit each other. Specifically, we develop a novel collaborative subspace learning strategy based on self-representation learning, which provides a brand-new way of pursuing the complete subspace structure directly from multi-view data. Furthermore, we explore complementary information by enforcing the consistency across different views and preserving the view-specific information of each view, which can alleviate the problem of feature degeneration and enhance the reasonability of using a consensus representation for multiple views. Experimental results on six benchmark datasets demonstrate that the proposed method can significantly outperform the state-of-the-art algorithms.

Unsupervised domain adaptation aims to leverage knowledge from a labeled source domain to learn an accurate model in an unlabeled target domain. However, many previous approaches propose to learn domain agnostic feature representations using a global distribution alignment objective, which does not consider the fine-grained cluster structures in the source and target domains. As such, the goal of this paper is to address two challenging problems:1) how to thoroughly explore fine-grained cluster structure knowledge in the source and target domains, 2) how to effectively incorporate these structure knowledge for adaptation.Regarding the first point, we are motivated by structural domain similarity assumption and propose structural representation learning, which is achieved by enforcing structural consistency between the source and target domains while retaining their individual discriminative properties. Regarding the second point, we firstly devise a novel structural centroid-based label prediction method, which explicitly models structural representations to form discriminative source and target cluster centroids, and estimates the label distribution of each target sample through the cosine similarity between its corresponding target cluster centroid and all the other source cluster centroids. Then, we adopt clustering learning to incorporate these discriminative structure knowledge for adaptation by minimizing the KL divergence between the predictive target label distribution and an introduced auxiliary one. Comprehensive experiments and analyses on four benchmark datasets demonstrate the superiority of the proposed discriminative clustering framework.

Multiview subspace learning has attracted much attention due to the efficacy of exploring the information on multiview features. Most existing methods perform data reconstruction on the original feature space and thus are vulnerable to noisy data. In this article, we propose a novel multiview subspace learning method, called multiview consensus structure discovery (MvCSD). Specifically, we learn the low-dimensional subspaces corresponding to different views and simultaneously pursue the structure consensus over subspace clustering for multiple views. In such a way, latent subspaces from different views regularize each other toward a common consensus that reveals the underlying cluster structure. Compared to existing methods, MvCSD leverages the consensus structure derived from the subspaces of diverse views to better exploit the intrinsic complementary information that well reflects the essence of data. Accordingly, the proposed MvCSD is capable of producing a more robust and accurate representation structure which is crucial for multiview subspace learning. The proposed method can be optimized effectively, with theoretical convergence guarantee, by alternatively iterating the argument Lagrangian multiplier algorithm and the eigendecomposition. Extensive experiments on diverse datasets demonstrate the advantages of our method over the state-of-the-art methods.

Cross-modal retrieval has caught appealing attentions as it supports querying across different modalities. However, most existing methods have emphasized on directly mapping heterogeneous features into the common subspace, which inevitably results in highly entangled representations, thereby preventing them from bridging the modality gap. This paper presents a novel deep framework called Controlled Semantic Embedding (CSE), which is the first attempt to learn disentangled representations with controlled semantic structure for cross-modal retrieval. Specifically, we design two generative networks based on variational autoencoder, which incorporate semantic discriminators for effective prediction of structured semantics. Meanwhile, a self-supervised semantic network is seamlessly integrated into the generative networks to supervise the semantic embedding process, which is further coupled with a quantizer for controlling the quantizability of semantic representations. Extensive experiments show the superiority of CSE over other state-of-the-art methods in cross-modal retrieval.

Transfer learning aims to learn an effective classifier for unlabeled target data by borrowing knowledge from well-labeled source data. However, most existing work has emphasized on learning domain invariant features to reduce the distribution discrepancy, which may suffer from the negative transfer problem caused by structure inconsistencies or distribution outliers. To address this challenge, in this paper, we propose a novel transfer learning approach, which seamlessly integrates domain invariant feature learning, discriminative structure preservation and sample reweighting into a unified learning model. Specifically, we attempt to learn domain invariant features by jointly adapting the marginal and conditional distributions. To transfer discriminative knowledge inferred from data, we enforce the structure consistency between the original feature space and the latent feature space. Furthermore, to enhance the robustness of our model, an efficient and more generalized sample reweighting strategy is developed to assign target predictions with different levels of confidence. The key advantage over previous methods is that our model can adaptively select pivot samples in target domain and retain the properties of discriminative structures underlying data domains, which enables coupled knowledge transfer during the learning process. Experimental results on several benchmark datasets have verified the superiority of the proposed method over other state-of-the-art algorithms.

Hashing-based techniques have provided attractive solutions to cross-modal similarity search when addressing vast quantities of multimedia data. However, existing cross-modal hashing (CMH) methods face two critical limitations: 1) there is no previous work that simultaneously exploits the consistent or modality-specific information of multi-modal data; 2) the discriminative capabilities of pairwise similarity is usually neglected due to the computational cost and storage overhead. Moreover, to tackle the discrete constraints, relaxation-based strategy is typically adopted to relax the discrete problem to the continuous one, which severely suffers from large quantization errors and leads to sub-optimal solutions. To overcome the above limitations, in this article, we present a novel supervised CMH method, namely Asymmetric Supervised Consistent and Specific Hashing (ASCSH). Specifically, we explicitly decompose the mapping matrices into the consistent and modality-specific ones to sufficiently exploit the intrinsic correlation between different modalities. Meanwhile, a novel discrete asymmetric framework is proposed to fully explore the supervised information, in which the pairwise similarity and semantic labels are jointly formulated to guide the hash code learning process. Unlike existing asymmetric methods, the discrete asymmetric structure developed is capable of solving the binary constraint problem discretely and efficiently without any relaxation. To validate the effectiveness of the proposed approach, extensive experiments on three widely used datasets are conducted and encouraging results demonstrate the superiority of ASCSH over other state-of-the-art CMH methods.

Convolution neural network (CNN) is an efficient technique to detect objects in various kinds of images, especially for microaneurysm (MA) of diabetic retinopathy in retinal fundus image. This study proposes a deconvolutional neural network to accurately discriminate MA from non‐MA. The deconvolution, instead of pooling operation, is embedded into the CNN to recover the erased details of feature maps of convolutional layers. Three types of images are collected for training and predicting. Furthermore, the extracted features are fed into the fully‐connected layers to classify using a softmax layer. Experimental results demonstrate that the proposed method can achieve significant sensitivity and accuracy on multiple public datasets, in comparison to the state‐of‐the‐art. For Retinopathy Online Challenge dataset, the sensitivity and accuracy are improved up to 0.798 and 0.986, respectively.

Projection learning is widely used in extracting discriminative features for classification. Although numerous methods have already been proposed for this goal, they barely explore the label information during projection learning and fail to obtain satisfactory performance. Besides, many existing methods can learn only a limited number of projections for feature extraction which may degrade the performance in recognition. To address these problems, we propose a novel constrained discriminative projection learning (CDPL) method for image classification. Specifically, CDPL can be formulated as a joint optimization problem over subspace learning and classification. The proposed method incorporates the low-rank constraint to learn a robust subspace which can be used as a bridge to seamlessly connect the original visual features and objective outputs. A regression function is adopted to explicitly exploit the class label information so as to enhance the discriminability of subspace. Unlike existing methods, we use two matrices to perform feature learning and regression, respectively, such that the proposed approach can obtain more projections and achieve superior performance in classification tasks. The experiments on several datasets show clearly the advantages of our method against other state-of-the-art methods.

Existing hashing methods barely explore the information loss problem during learning the common semantic subspace, thus retrieval performance may be degraded. Besides, these methods mainly rely on the inter-modality or intra-modality correlations separately and fail to exploit the full structure reflected by these correlations. To address these problems, we present a novel cross-modal hashing method, namely Robust Multi-View Hashing (RMVH). To learn a robust latent semantic subspace, we enforce the learnt representations to well reconstruct original features such that more important information can be retained. To comprehensively exploit the relationship between representations of multiple modalities, we utilize Multi-View Learning to construct an affinity matrix to guide the learning of common latent semantic subspace, which can preserve both inter-modality and intra-modality similarities. Instead of relaxing the binary constraints, we leverage the label information to learn hash codes discretely which can avoid the large quantization error and preserve the semantic similarity. Experimental results on three benchmark datasets show that the proposed RMVH achieves superior performance compared with other state-of-the-art methods.

Most existing methods seek for the common semantics using different projections for different modalities, which isolates the intrinsic relationships among different modalities. Besides, to avoid the large quantization error, some of them adopt the discrete cyclic coordinate descent schemes which are usually time-consuming. To address these issues, we present a novel hashing method, namely Supervised Consistent and Specific Hashing (SCSH), for cross-modal retrieval. We explicitly decompose the mapping matrices into consistent part and modality-specific ones. Specifically, consistency excavates the semantic shared by different modalities, whereas specificity captures private properties for each modality. Different from prior works, SCSH can discover the intrinsic semantic shared among different modalities more accurately. Moreover, by regressing the semantic labels to hash codes, SCSH can further promote the discriminative power of hash codes and significantly accelerate the hashing learning process. Extensive experiments on three widely used datasets demonstrate that the proposed SCSH outperforms other state-of-the-art methods.

Zero-shot learning (ZSL) for visual recognition aims to accurately recognize the objects of unseen classes through mapping the visual feature to an embedding space spanned by class semantic information. However, the semantic gap across visual features and their underlying semantics is still a big obstacle in ZSL. Conventional ZSL methods construct that the mapping typically focus on the original visual features that are independent of the ZSL tasks, thus degrading the prediction performance. In this paper, we propose an effective method to uncover an appropriate latent representation of data for the purpose of zero-shot classification. Specifically, we formulate a novel framework to jointly learn the latent subspace and cross-modal embedding to link visual features with their semantic representations. The proposed framework combines feature learning and semantics prediction, such that the learned data representation is more discriminative to predict the semantic vectors, hence improving the overall classification performance. To learn a robust latent subspace, we explicitly avoid the information loss by ensuring the reconstruction ability of the obtained data representation. An efficient algorithm is designed to solve the proposed optimization problem. To fully exploit the intrinsic geometric structure of data, we develop a manifold regularization strategy to refine the learned semantic representations, leading to further improvements of the classification performance. To validate the effectiveness of the proposed approach, extensive experiments are conducted on three ZSL benchmarks and encouraging results are achieved compared with the state-of-the-art ZSL methods.

In this letter, we propose a novel low-rank-representation (LRR) based manifold-regularization approach for zero-shot learning (ZSL). Most existing regularization-based ZSL approaches perform the alignment between visual feature space and semantic space based on the affinity matrix constructed from the test instances. The affinity matrix plays a significant role in exploiting the manifold structures of visual feature space, hence we propose to use the LRR to guide the affinity-matrix construction by exploring the subspace structures of data. Considering the locality and similarity information among data, we incorporate a Laplacian regularization term to the LRR framework to ensure that the learned affinity matrix can capture the local geometric structures in data. We also explicitly impose the nonnegative sparse constraint on the affinity matrix to facilitate the learning of local manifold structures. Moreover, we use an effective manifold-regularization methodology to learn discriminative semantic representations of test instances, leading to significant improvements in classification performance over the unseen classes. Extensive experiments on three benchmark datasets demonstrate that the proposed approach outperforms the state of the arts.

ABSTRACT We present a novel sketch‐based tool, called iCutter (short for intelligent cutter), for cutting out semantic parts of 3D shapes. When a user performs a cutting task, he only needs to draw a freehand stroke to roughly specify where cuts should be made without much attention. Then, iCutter intelligently returns the best cut that meets the user's intention and expectation. We develop a novel scheme for selecting the optimal isoline from a well‐designed scalar field induced from the input stroke, which respects the part saliency as well as the input stroke. We demonstrate various examples to illustrate the flexibility and applicability of our iCutter tool. Copyright © 2011 John Wiley & Sons, Ltd.

While many 3D objects exhibit various forms of global symmetries, prominent intrinsic symmetries which exist only on parts of an object are also well recognized. Such partial symmetries are often seen as more natural than a global one, even when the symmetric parts are under complex pose. We introduce an algorithm to extract partial intrinsic reflectional symmetries (PIRS) of a 3D shape. Given a closed 2-manifold mesh, we develop a voting scheme to obtain an intrinsic reflectional symmetry axis (IRSA) transform, which is a scalar field over the mesh that accentuates prominent IRSAs of the shape. We then extract a set of explicit IRSA curves on the shape based on a refined measure of local reflectional symmetry support along a curve. The iterative refinement procedure combines IRSA-induced region growing and region-constrained symmetry support refinement to improve accuracy and address potential issues arising from rotational symmetries in the shape. We show how the extracted IRSA curves can be incorporated into a conventional mesh segmentation scheme so that the implied symmetry cues can be utilized to obtain more meaningful results. We also demonstrate the use of IRSA curves for symmetry-driven part repair.