Large Language Models (LLMs) show remarkable potential for few-shot information extraction (IE), yet their performance is highly sensitive to the choice of in-context examples. Conventional selection strategies often fail to provide informative guidance, as they overlook a key source of model fallibility: confusion stemming not just from semantic content, but also from the generation of well-structured formats required by IE tasks. To address this, we introduce Active Prompting for Information Extraction (APIE), a novel active prompting framework guided by a principle we term introspective confusion. Our method empowers an LLM to assess its own confusion through a dual-component uncertainty metric that uniquely quantifies both Format Uncertainty (difficulty in generating correct syntax) and Content Uncertainty (inconsistency in extracted semantics). By ranking unlabeled data with this comprehensive score, our framework actively selects the most challenging and informative samples to serve as few-shot exemplars. Extensive experiments on four benchmarks show that our approach consistently outperforms strong baselines, yielding significant improvements in both extraction accuracy and robustness. Our work highlights the critical importance of a fine-grained, dual-level view of model uncertainty when it comes to building effective and reliable structured generation systems.

Recent studies have demonstrated that semi-supervised learning (SSL) is highly vulnerable to backdoor attacks, where adversaries can manipulate up to 90% of model predictions through just a tiny fraction of poisoned training data. Despite the widespread adoption of SSL in safety-critical applications, effective defenses against such attacks remain limited. In this paper, we present a comprehensive defense framework designed to protect SSL against sophisticated backdoor attacks. Our work begins with a systematic analysis of backdoor mechanisms in SSL from two critical perspectives: (1) how attackers establish persistent correlations between triggers and target classes; (2) how triggers are introduced and resist removal at the data level. Our investigation reveals that, unlike supervised learning, SSL backdoor attacks (1) uniquely exploit pseudo-labeling mechanisms to establish stronger trigger-target correlations, and (2) demonstrate remarkable resilience at the data level, with triggers potentially appearing in any frequency band (low, medium, or high). Based on these insights, we introduce Backdoor Invalidator (BI), a defense framework that integrates three novel techniques: complementary learning, trigger mix-up, and dual domain filtering, which collectively obstruct, dilute, and filter the influence of backdoor attacks in both feature learning and data processing. Through extensive evaluation against state-of-the-art attacks, BI significantly reduces the average attack success rate while maintaining comparable accuracy on clean data. We also provide theoretical guarantees for BI’s generalization capability and demonstrate its practical deployability as a plug-in component. The code of this work is available at https://github.com/wxr99/Backdoor Invalidator4SSL.

The open set known class bias is conventionally viewed as a fatal problem i.e., the models trained solely on known classes tend to fit unknown classes to known classes with high confidence in inference. Thus existing methods, without exception make a choice in two manners: most methods opt for eliminating the known class bias as much as possible with tireless efforts, while others circumvent the known class bias by employing a reconstruction method. However, in this paper, we challenge the two widely accepted approaches and present a novel proposition: the so-called harmful known class bias for most methods is, exactly conversely, beneficial for the reconstruction-based method and thus such known class bias can serve as a positive-incentive to the Open set recognition (OSR) models from a reconstruction perspective. Along this line, we propose the Bias Enhanced Reconstruction Learning (BERL) framework to enhance the known class bias respectively from the class level, model level and sample level. Specifically, at the class level, a specific representation is constructed in a supervised contrastive manner to avoid overgeneralization, while a diffusion model is employed by injecting the class prior to guide the biased reconstruction at the model level. Additionally, we leverage the advantages of the diffusion model to design a self-adaptive strategy, enabling effective sample-level biased sampling based on the information bottleneck theory. Experiments on various benchmarks demonstrate the effectiveness and performance superiority of the proposed method.

In recent Open-set Recognition (OSR) community, a prevailing belief is that enhancing the discriminative boundaries of closed-set classes can improve the robustness of Deep Neural Networks (DNNs) against open data during testing. Typical studies validate this *implicitly* by empirical evidence, without a formalized understanding of *how DNNs help the closed-set features obtain more discriminative boundaries?* For this, we provide an answer from the Neural Collapse (NC) perspective: DNNs align the closed-set with a *Simplex Equiangular Tight Frame* (ETF) structure that has geometric and mathematical interpretability. Regrettably, although NC naturally occurs in DNNs, we discover that typical studies cannot guarantee the features being learned to strictly align with the ETF. Thus, we introduce a novel concept, Fixed ETF Template (FiT), which holds an ideal structure associated with closed-set classes. To force class means and classifier vectors to align with FiT, we further design a Dual ETF (DEF) loss involving two components. Specifically, *F*-DEF loss is designed to align class means with FiT strictly, yielding optimal inter-class separability. Meanwhile, we extend a dual form to classifier vectors, termed *C*-DEF loss, which guides class means and classifier vectors to satisfy self-duality. Our theoretical analysis proves the validity of the proposed approach, and extensive experiments demonstrate that DEF achieves comparable or superior results with reduced computational resources on standard OSR benchmarks.

Existing few-shot open-set recognition (FSOR) methods primarily employ the meta-learning mechanism, in which each meta-task randomly selects a small subset of base classes as knowns, and samples an equal number of classes from the remaining base classes as pseudo-unknowns. While effective, these methods potentially face two critical weaknesses: i) Class-identity overlapping: The same classes are designated as knowns in one meta-task but may be considered as pseudo-unknowns in another, leading to conflicts across meta-tasks and consequently degrading the model’s performance; ii) Narrow pseudo-unknown utilization: Each meta-task selects only a limited number of base classes as pseudo-unknowns rather than providing a broader view of more available base classes. Fundamentally, these issues arise from the myopia of the meta-learners in existing methods, as they lack a broad view on all available base classes. To this end, we strategically propose a novel Hyperopia Distillation Enhancement framework (HDE) for FSOR, which encourages the meta-learner to observe a broader view of pseudo-unknown classes without too worrying about the class-imbalance issue, while effectively mitigating the class-identity overlapping problem. The key to HDE lies in its dual hyperopia distillation mechanism, which enhances the meta-learner by hyperopically distilling available full-view inter-class relationships and more pseudo-unknown knowledge. Extensive experiments verify the effectiveness of our HDE.

Using unlabeled wild data containing both in-distribution (ID) and out-of-distribution (OOD) data to improve the safety and reliability of models has recently received increasing attention. Existing methods either design customized losses for labeled ID and unlabeled wild data then perform joint optimization, or first filter out OOD data from the latter then learn an OOD detector. While achieving varying degrees of success, two potential issues remain: (i) Labeled ID data typically dominates the learning of models, inevitably making models tend to fit OOD data as IDs; (ii) The selection of thresholds for identifying OOD data in unlabeled wild data usually faces dilemma due to the unavailability of pure OOD samples. To address these issues, we propose a novel loss-difference OOD detection framework (LoD) by intentionally label-noisifying unlabeled wild data. Such operations not only enable labeled ID data and OOD data in unlabeled wild data to jointly dominate the models' learning but also ensure the distinguishability of the losses between ID and OOD samples in unlabeled wild data, allowing the classic clustering technique (e.g., K-means) to filter these OOD samples without requiring thresholds any longer. We also provide theoretical foundation for LoD's viability, and extensive experiments verify its superiority.

The need to generalize the pre-trained deep learning models to unknown test-time data distributions has spurred research into test-time adaptation (TTA). Existing studies have mainly focused on closed-set TTA with only covariate shifts, while largely overlooking open-set TTA that involves semantic shifts, i.e., unknown open-set classes. However, addressing adaptation to unknown classes is crucial for open-world safety-critical applications such as autonomous driving. In this paper, we emphasize that accurate identification of the open-set samples is rather challenging in TTA. The entanglement of semantic shift and covariate shift mutually confuse the network’s discriminative capability. This co-interference further exacerbates considering the single-pass data nature and low latency requirements. With this under standing, we propose Dual-mode Matching and Prompt-based Open Set Adaptation (DM-POSA) for open-set TTA to enhance discriminative feature learning and unknown classes distinguishment with minimal time cost. DM-POSA identifies open-set samples via dual-mode matching strategies, including model-parameter-based and feature space-based matching. It also optimizes the model with a random pairing discrepancy loss, enhancing the distributional difference between open-set and closed-set samples, thus improving the model’s ability to recognize unknown categories. Extensive experiments show the superiority of DM-POSA over state-of-the-art baselines on both closed-set class adaptation and open-set class detection.

Consistency and complementarity are two key ingredients for boosting multi-view clustering (MVC). Recently with the introduction of popular contrastive learning, the consistency learning of views has been further enhanced in MVC, leading to promising performance. However, by contrast, the complementarity has not received sufficient attention except just in the feature facet, where the Hilbert Schmidt Independence Criterion term or the independent encoder-decoder network is usually adopted to capture view-specific information. This motivates us to reconsider the complementarity learning of views comprehensively from multiple facets including the feature-, view-label- and contrast- facets, while maintaining the view consistency. We empirically find that all the facets contribute to the complementarity learning, especially the view-label facet, which is usually neglected by existing methods. Based on this, a simple yet effectiveMultifacetComplementarity learning framework forMulti-ViewClustering (MCMVC) is naturally developed, which fuses multifacet complementarity information, especially explicitly embedding the view-label information. To our best knowledge, it is the first time to use view-labels explicitly to guide the complementarity learning of views. Compared with the SOTA baselines, MCMVC achieves remarkable improvements, e.g., by average margins over 5.00% and 7.00% respectively in complete and incomplete MVC settings on Caltech101-20 in terms of three evaluation metrics.

Neural Collapse (NC) presents an elegant geometric structure that enables individual activations (features), class means and classifier (weights) vectors to reachoptimalinter-class separability during the terminal phase of training on abalanceddataset. Once shifted to imbalanced classification, such an optimal structure of NC can be readily destroyed by the notoriousminority collapse, where the classifier vectors corresponding to the minority classes are squeezed. In response, existing works mainly optimize classifiers in an effort to recover NC. However, we discover that this squeezing phenomenon is not only confined to classifier vectors but also occurs with class means. Consequently, reconstructing NC solely at the classifier aspect may be futile, as the class means remain compressed, leading to the violation of inherentself-dualityin NC (i.e., class means and classifier vectors converge mutually) and incidentally, an unsatisfactory collapse of individual activations towards the corresponding class means. To shake off these dilemmas, we present a unifiedAll-aroundNeuralCollapse framework (AllNC), aiming to comprehensively restore NC across multiple aspects including individual activations, class means and classifier vectors. We thoroughly analyze its effectiveness and verify its performance on multiple benchmark datasets as state-of-the-art in both balanced and imbalanced settings.

In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data outside given K known classes as an additional K+1-th class to explicitly model potential open space. However, treating unknown classes without distinction is unequal for them relative to known classes due to the category-agnostic and scale-agnostic of the unknowns. This inevitably not only disrupts the inherent distributions of unknown classes but also incurs both class-wise and instance-wise imbalances between known and unknown classes. Ideally, the OSR problem should model the whole class space as K+∞, but enumerating all unknowns is impractical. Since the core of OSR is to effectively model the boundaries of known classes, this means just focusing on the unknowns nearing the boundaries of targeted known classes seems sufficient. Thus, as a compromise, we convert the open classes from infinite to K, with a novel concept Target-Aware Universum (TAU) and propose a simple yet effective framework Dual Contrastive Learning with Target-Aware Universum (DCTAU). In details, guided by the targeted known classes, TAU automatically expands the unknown classes from the previous 1 to K, effectively alleviating the distribution disruption and the imbalance issues mentioned above. Then, a novel Dual Contrastive (DC) loss is designed, where all instances irrespective of known or TAU are considered as positives to contrast with their respective negatives. Experimental results indicate DCTAU sets a new state-of-the-art.

Online continual learning (OCL) requires the models to learn from constant, endless streams of data. While significant efforts have been made in this field, most were focused on mitigating the \textit{catastrophic forgetting} issue to achieve better classification ability, at the cost of a much heavier training workload. They overlooked that in real-world scenarios, e.g., in high-speed data stream environments, data do not pause to accommodate slow models. In this paper, we emphasize that \textit{model throughput}-- defined as the maximum number of training samples that a model can process within a unit of time -- is equally important. It directly limits how much data a model can utilize and presents a challenging dilemma for current methods. With this understanding, we revisit key challenges in OCL from both empirical and theoretical perspectives, highlighting two critical issues beyond the well-documented catastrophic forgetting: (\romannumeral1) Model's ignorance: the single-pass nature of OCL challenges models to learn effective features within constrained training time and storage capacity, leading to a trade-off between effective learning and model throughput; (\romannumeral2) Model's myopia: the local learning nature of OCL on the current task leads the model to adopt overly simplified, task-specific features and \textit{excessively sparse classifier}, resulting in the gap between the optimal solution for the current task and the global objective. To tackle these issues, we propose the Non-sparse Classifier Evolution framework (NsCE) to facilitate effective global discriminative feature learning with minimal time cost. NsCE integrates non-sparse maximum separation regularization and targeted experience replay techniques with the help of pre-trained models, enabling rapid acquisition of new globally discriminative features. Extensive experiments demonstrate the substantial improvements of our framework in performance, throughput and real-world practicality.

Abstract Anomaly detection is a method employed to identify data points or patterns that significantly deviate from expected or normal behaviour within a dataset. This approach aims to detect observations regarded as unusual, erroneous, anomalous, rare, or potentially indicative of fraudulent or malicious activity. Open‐set recognition, also referred to as open‐set identification or open‐set classification, is a pattern recognition task that extends traditional classification by addressing the presence of unknown or novel classes during the testing phase. This approach highlights a strong connection between anomaly detection and open‐set recognition, as both seek to identify samples originating from unknown classes or distributions. Open‐set recognition methods frequently involve modelling both known and unknown classes during training, allowing for the capture of the distribution of known classes while explicitly addressing the space of unknown classes. Techniques in open‐set recognition may include outlier detection, density estimation, or configuring decision boundaries to better differentiate between known and unknown classes. This special issue calls for original contributions introducing novel datasets, innovative architectures, and advanced training methods for tasks related to visual anomaly detection and open‐set recognition.

Logit adjustment is an effective long-tailed visual recognition strategy to encourage a significant margin between rare and dominant labels. Existing methods typically employ the globally fixed label frequencies throughout the training to adjust margins. However, in practice, we observe that the local (in-batch) label frequencies change dynamically or even vanish for some classes (especially the tail classes) in batch-dependent training, which is inconsistent with global ones. Furthermore, our analyses reveal that the intra-classcollinear samplesactually do not contribute to the gradient update, but substantially increase the corresponding local label frequencies. Such contributions are spurious due to over-counting the label frequencies without contributing to the gradient. All of these will cause serious interference in precisely estimating local frequencies of the authentic contribution, leading to inauthentic margins. To simultaneously address the above issues, this paper innovatively proposes Dynamic Learnable Logit Adjustment (DLLA) loss to learn the local label frequencies within dynamic mini-batches precisely. Specifically, DLLA owns two complementary parts: 1)rank-metriceliminates spurious contributions from collinear samples by calculating the algebraic rank of the feature subspace in the mini-batch. 2)class-supplementensures all classes appear in every mini-batch by inserting the corresponding learnable class prototype, for which we resort to neural collapse theory to make them align to the ideal regular simplex structure. Extensive experiments on standard benchmark datasets verify the effectiveness of our method.

In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch. Recognizers in decision either reject or categorize them to some known class using empirically-set threshold. Thus the decision threshold plays a key role. However, the selection for it usually depends on the knowledge of known classes, inevitably incurring risks due to lacking available information from unknown classes. On the other hand, a more realistic OSR system should NOT just rest on a reject decision but should go further, especially for discovering the hidden unknown classes among the reject instances, whereas existing OSR methods do not pay special attention. In this paper, we introduce a novel collective/batch decision strategy with an aim to extend existing OSR for new class discovery while considering correlations among the testing instances. Specifically, a collective decision-based OSR framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet process (HDP). Thanks to HDP, our CD-OSR does not need to define the decision threshold and can implement the open set recognition and new class discovery simultaneously. Finally, extensive experiments on benchmark datasets indicate the validity of CD-OSR.

Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like accumulated errors and increased estimation bias persist due to the absence of negative labels. In this paper, we unveil an intriguing yet long-overlooked observation in PUL: \textit{resampling the positive data in each training iteration to ensure a balanced distribution between positive and unlabeled examples results in strong early-stage performance. Furthermore, predictive trends for positive and negative classes display distinctly different patterns.} Specifically, the scores (output probability) of unlabeled negative examples consistently decrease, while those of unlabeled positive examples show largely chaotic trends. Instead of focusing on classification within individual time frames, we innovatively adopt a holistic approach, interpreting the scores of each example as a temporal point process (TPP). This reformulates the core problem of PUL as recognizing trends in these scores. We then propose a novel TPP-inspired measure for trend detection and prove its asymptotic unbiasedness in predicting changes. Notably, our method accomplishes PUL without requiring additional parameter tuning or prior assumptions, offering an alternative perspective for tackling this problem. Extensive experiments verify the superiority of our method, particularly in a highly imbalanced real-world setting, where it achieves improvements of up to $11.3\%$ in key metrics.

As an effective data augmentation method, Mixup synthesizes an extra amount of samples through linear interpolations. Despite its theoretical dependency on data properties, Mixup reportedly performs well as a regularizer and calibrator contributing reliable robustness and generalization to deep model training. In this paper, inspired by Universum Learning which uses out-of-class samples to assist the target tasks, we investigate Mixup from a largely under-explored perspective - the potential to generate in-domain samples that belong to none of the target classes, that is, universum. We find that in the framework of supervised contrastive learning, Mixup-induced universum can serve as surprisingly high-quality hard negatives, greatly relieving the need for large batch sizes in contrastive learning. With these findings, we propose Universum-inspired supervised Contrastive learning (UniCon), which incorporates Mixup strategy to generate Mixup-induced universum as universum negatives and pushes them apart from anchor samples of the target classes. We extend our method to the unsupervised setting, proposing Unsupervised Universum-inspired contrastive model (Un-Uni). Our approach not only improves Mixup with hard labels, but also innovates a novel measure to generate universum data. With a linear classifier on the learned representations, UniCon shows state-of-the-art performance on various datasets. Specially, UniCon achieves 81.7% top-1 accuracy on CIFAR-100, surpassing the state of art by a significant margin of 5.2% with a much smaller batch size, typically, 256 in UniCon vs. 1024 in SupCon (Khosla et al., 2020) using ResNet-50. Un-Uni also outperforms SOTA methods on CIFAR-100. The code of this paper is released on https://github.com/hannaiiyanggit/UniCon.

As an effective method for XOR problems, generalized eigenvalue proximal support vector machine (GEPSVM) recently has gained widespread attention accompanied with many variants proposed. Although these variants strengthen the classification performance to different extents, the number of fitting hyperplanes, similar to GEPSVM, for each class is still limited to just one. Intuitively, using single hyperplane seems not enough, especially for the datasets with complex feature structures. Therefore, this article mainly focuses on extending the fitting hyperplanes for each class from single one to multiple ones. However, such an extension from the original GEPSVM is not trivial even though, if possible, the elegant solution via generalized eigenvalues will also not be guaranteed. To address this issue, we first make a simple yet crucial transformation for the optimization problem of GEPSVM and then propose a novel multiplane convex proximal support vector machine (MCPSVM), where a set of hyperplanes determined by the features of the data are learned for each class. We adopt a strictly (geodesically) convex objective to characterize this optimization problem; thus, a more elegant closed-form solution is obtained, which only needs a few lines of MATLAB codes. Besides, MCPSVM is more flexible in form and can be naturally and seamlessly extended to the feature weighting learning, whereas GEPSVM and its variants can hardly straightforwardly work like this. Extensive experiments on benchmark and large-scale image datasets indicate the advantages of our MCPSVM.

In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch. Recognizers in decision either reject or categorize them to some known class using empirically-set threshold. Thus the decision threshold plays a key role. However, the selection for it usually depends on the knowledge of known classes, inevitably incurring risks due to lacking available information from unknown classes. On the other hand, a more realistic OSR system should NOT just rest on a reject decision but should go further, especially for discovering the hidden unknown classes among the reject instances, whereas existing OSR methods do not pay special attention. In this paper, we introduce a novel collective/batch decision strategy with an aim to extend existing OSR for new class discovery while considering correlations among the testing instances. Specifically, a collective decision-based OSR framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet process (HDP). Thanks to HDP, our CD-OSR does not need to define the decision threshold and can implement the open set recognition and new class discovery simultaneously. Finally, extensive experiments on benchmark datasets indicate the validity of CD-OSR.

In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with unseen ones. This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria, and algorithm comparisons. Furthermore, we briefly analyze the relationships between OSR and its related tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification with reject option, and so forth. Additionally, we also review the open world recognition which can be seen as a natural extension of OSR. Importantly, we highlight the limitations of existing approaches and point out some promising subsequent research directions in this field.

For a multicategory classification problem, discriminative least squares regression (DLSR) explicitly introduces an -dragging technique to enlarge the margin between the categories, yielding superior classification performance from a margin perspective. In this brief, we reconsider this classification problem from a metric learning perspective and propose a framework of metric learning-guided least squares classifier (MLG-LSC) learning. The core idea is to learn a unified metric matrix for the error of LSR, such that such a metric matrix can yield small distances for the same category, while large ones for the different categories. As opposed to the -dragging in DLSR, we call this the error-dragging (e-dragging). Different from DLSR and its related variants, our MLG-LSC implicitly carries out the e-dragging and can naturally reflect the roughly relative distance relationships among the categories from a metric learning perspective. Furthermore, our optimization objective functions are strictly (geodesically) convex and thus can obtain their corresponding closed-form solutions, resulting in higher computational performance. Experimental results on a set of benchmark data sets indicate the validity of our learning framework.