Unknown Categories Research Articles

Open set identification of malicious encrypted traffic based on multi-feature fusion

In the current network environment, an increasing amount of malicious traffic is transmitted through encrypted channels, carrying control commands and data. With the continuous development of communication protocols and applications, new types of malicious encrypted traffic are emerging, posing significant challenges for network management (e.g., traffic engineering). Therefore, accurately identifying malicious traffic in complex open network spaces has become a hot research topic in network security. In this study, we draw inspiration from channel theory in image science and innovatively convert traffic data into Red-Green-Blue (RGB) image format to achieve the fusion of multiple features. Inspired by image recognition technologies, we have designed a multi-granularity network model that integrates both global and local features, serving as our core network architecture. At the top of the model, we have equipped each known category with a unique autoencoder, using its generated manifold to replace traditional prototypes for model construction. Classification is accomplished through a scoring mechanism that evaluates category membership and by setting thresholds to achieve open set recognition of unknown categories. Relying on our self-created dataset,Malicious and Encrypted Traffic 2024 (MNET2024), we conduct a series of extensive experiments. The results demonstrate that our proposed method exhibits outstanding performance in both closed-set and open-set recognition tasks.

A Zero-Shot Learning Approach for Blockage Detection and Identification Based on the Stacking Ensemble Model.

A data-driven approach to defect identification requires many labeled samples for model training. Yet new defects tend to appear during data acquisition cycles, which can lead to a lack of labeled samples of these new defects. Aiming at solving this problem, we proposed a zero-shot pipeline blockage detection and identification method based on stacking ensemble learning. The experimental signals were first decomposed using variational modal decomposition (VMD), and then, the information entropy was calculated for each intrinsic modal function (IMF) component to construct the feature sets. Second, the attribute matrix was established according to the attribute descriptions of the defect categories, and the stacking ensemble attribute learner was used for the attribute learning of defect features. Finally, defect identification was accomplished by comparing the similarity within the attribute matrices. The experimental results show that target defects can be identified even without targeted training samples. The model showed better classification performance on the six sets of experimental data, and the average recognition accuracy of the model for unknown defect categories reached 72.5%.

Pseudo-unknown uncertainty learning for open set object detection

Despite the significant strides made by modern object detectors in the closed-set scenarios, open-set object detection (OSOD) remains a formidable challenge. This is particularly evident in misclassifying objects from unknown categories into pre-existing known classes or ignored background classes. A novel approach called PUDet (Pseudo-unknown Uncertainty Detector) based on Evidential Deep Learning (EDL) is proposed, incorporating two modules: the Class-wise Contrastive Learning Network (CCL) and the Uncertainty-Aware Labeling Network (UAL). For CCL, the module leverages class-wise contrastive learning to encourage intra-class compactness and inter-class separation, thereby reducing the overlap between known and unknown classes. Simultaneously, it establishes compact boundaries for known classes and generates pseudo-unknown candidates to facilitate UAL for better learning pseudo-unknown uncertainty. For UAL, the Weight-Impact EDL (WI-EDL) approach is introduced to enhance uncertainty in edge samples by collecting categorical evidence and weight impact. Subsequently, UAL refines uncertainty via localization quality calibration, facilitating the mining of pseudo-unknown samples from foreground and background proposals to construct compact boundaries between known and unknown categories. In comparison to the state of the arts, the proposed PUDet showcases a substantial improvement, achieving a reduction in Absolute Open-Set Errors by 13%–16% across six OSOD benchmarks.

Detection of Novel Objects without Fine-Tuning in Assembly Scenarios by Class-Agnostic Object Detection and Object Re-Identification

Object detection is a crucial capability of autonomous agents for human–robot collaboration, as it facilitates the identification of the current processing state. In industrial scenarios, it is uncommon to have comprehensive knowledge of all the objects involved in a given task. Furthermore, training during deployment is not a viable option. Consequently, there is a need for a detector that is able to adapt to novel objects during deployment without the necessity of retraining or fine-tuning on novel data. To achieve this, we propose to exploit the ability of discriminative embeddings learned by an object re-identification model to generalize to unknown categories described by a few shots. To do so, we extract object crops with a class-agnostic detector and then compare the object features with the prototypes of the novel objects. Moreover, we demonstrate that the embedding is also effective for predicting regions of interest, which narrows the search space of the class-agnostic detector and, consequently, increases processing speed. The effectiveness of our approach is evaluated in an assembly scenario, wherein the majority of objects belong to categories distinct from those present in the training datasets. Our experiments demonstrate that, in this scenario, our approach outperforms the current best few-shot object-detection approach DE-ViT, which also does not perform fine-tuning on novel data, in terms of both detection capability and inference speed.

Cross-domain few-shot semantic segmentation for the astronaut work environment

The study of few-shot semantic segmentation (FSS) for the astronaut work environment (AWE) is of significant importance as it enables the segmentation of unknown categories. However, general FSS methods are predicated on the assumption that the training and testing data belong to the same domain. When this assumption is invalid, the model’s performance is significantly degraded. We propose a more general approach, whereby the model is trained on a generic dataset and tested on a dedicated AWE dataset. This challenging task is referred to as cross-domain few-shot semantic segmentation (CD-FSS). A novel model, namely FTDCNet, is proposed, which comprises a domain-agnostic feature transformation module and a domain-constrained transformer. The FTDCNet model demonstrates superior performance compared to the state-of-the-art (SOTA) model, with an accuracy improvement of 11.83% and 11.42% under 1-shot and 5-shot settings, respectively.

Embedding enhancement with foreground feature alignment and primitive knowledge for few-shot learning

Few-Shot Learning (FSL) targets a model to quickly discriminate new categories with limited samples. While most methods struggle to effectively utilize the knowledge learned in the base classes, resulting in models that fail to alleviate the domain gap between training and evaluation. Conversely, humans often enable rapid discrimination by two ways: reinforcing impressions through comparing unknown categories with related objects in memory; Searching for the maximum shared information within the intra-class. Motivated by this, in this paper, we propose a novel Transformer-based Embedding Enhancement Network (TEEN) that adaptively leverages the knowledge learned from the base classes, which we refer to as ‘primitive knowledge’, to complete and distinguish the embeddings for novel classes. Additionally, to reduce interference from background features, we introduce the Transformer-based Foreground Feature Alignment (TFFA) to enhance the representation of image foreground. Ultimately, TEEN enhances the embeddings through foreground alignment and primitive knowledge completion, achieving improved performance in few-shot classification. Extensive experiments on inductive few-shot tasks demonstrate the effectiveness of our approach, achieving state-of-the-art results in cross-domain few-shot tasks.

SegLD: Achieving universal, zero-shot and open-vocabulary segmentation through multimodal fusion via latent diffusion processes

Open-vocabulary learning can identify categories marked during training (seen categories) and generalize to categories not annotated in the training set (unseen categories). It could theoretically extend segmentation systems to more universal applications. However, current open-vocabulary segmentation frameworks are primarily suited for specific tasks or require retraining according to the task, and they significantly underperform in inferring seen categories compared to fully supervised frameworks. Therefore, we introduce a universal open-vocabulary segmentation framework based on the latent diffusion process (SegLD), which requires only a single training session on a panoptic dataset to achieve inference across all open-vocabulary segmentation tasks, and reaches SOTA segmentation performance for both seen and unseen categories in every task. Specifically, SegLD comprises two stages: in the first stage, we deploy two parallel latent diffusion processes to deeply fuse the text (image caption or category labels) and image information, further aggregating the multi-scale features output from both latent diffusion processes on a scale basis. In the second stage, we introduce text queries, text list queries, and task queries, facilitating the learning of inter-category and inter-task differences through the computation of contrastive losses between them. Text queries are then further fed into a Transformer Decoder to obtain category-agnostic segmentation masks. Then we establish classification loss functions for the type of text input during training, whether image captions or category labels, to help assign a category label from the open vocabulary to each predicted binary mask. Experimental results show that, with just a single training session, SegLD significantly outperforms other contemporary SOTA fully supervised segmentation frameworks and open-vocabulary segmentation frameworks across almost all evaluation metrics for both known and unknown categories on the ADE20K, Cityscapes, and COCO datasets. This highlights SegLD’s capability as a universal segmentation framework, with the potential to replace other segmentation frameworks and adapt to various segmentation domains. The project link for SegLD is https://zht-segld.github.io/.

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

In recent years, the defect image classification method based on deep transfer learning has been widely explored and researched, and the task of source and target domains with the same painting defect image class has been solved successfully. However, in real applications, due to the complexity and uncertainty of ship painting conditions, it is very likely that there are unknown classes of painting defects, and the traditional deep learning model cannot identify a few classes, which leads to model overfitting and reduces its generalization ability. In this paper, a zero-shot Image classification method for ship painting defects based on IDATLWGAN is proposed to identify new unknown classes of defects in the target domain. The method is based on a deep convolutional neural network combined with adversarial transfer learning. First, a preprocessed ship painting defect dataset is used as input for the domain-invariant feature extractor. Then, the domain invariant feature extractor takes domain invariant features from the source and target domains. Finally, Defect discriminators and domain alignment discriminators are employed to classify the known categories of unlabeled defects and unknown categories of unlabeled defects in the target domain and to further reduce the distance between the edge distributions of the source and target domains. The experimental results show that the proposed model in this paper extracts a better distribution of invariant features in the source and target domains compared to other existing transfer learning models. It can successfully complete the migration task and accurately recognize the painting defects of known categories and new unknown categories, which is a perfect combination of intelligent algorithms and engineering practice.

Generative Adversarial Network and Mutual-Point Learning Algorithm for Few-Shot Open-Set Classification of Hyperspectral Images

Existing approaches addressing the few-shot open-set recognition (FSOSR) challenge in hyperspectral images (HSIs) often encounter limitations stemming from sparse labels, restricted category numbers, and low openness. These limitations compromise stability and adaptability. In response, an open-set HSI classification algorithm based on data wandering (DW) is introduced in this research. Firstly, a K-class classifier suitable for a closed set is trained, and its internal encoder is leveraged to extract features and estimate the distribution of known categories. Subsequently, the classifier is fine-tuned based on feature distribution. To address the scarcity of samples, a sample density constraint based on the generative adversarial network (GAN) is employed to generate synthetic samples near the decision boundary. Simultaneously, a mutual-point learning method is incorporated to widen the class distance between known and unknown categories. In addition, a dynamic threshold method based on DW is devised to enhance the open-set performance. By categorizing drifting synthetic samples into known and unknown classes and retraining them together with the known samples, the closed-set classifier is optimized, and a (K + 1)-class open-set classifier is trained. The experimental results in this research demonstrate the superior FSOSR performance of the proposed method across three benchmark HSI datasets.

Myocardial injury in dogs: a retrospective analysis on etiological, echocardiographic, electrocardiographic, therapeutic, and outcome findings in 102 cases

IntroductionIn dogs, myocardial injury (MI) is a poorly characterized clinical entity; therefore, this study aimed to provide a detailed description of dogs affected by this condition. Animals, materials, and methodsDogs diagnosed with MI according to the concentration of cardiac troponin I (cTnI) were retrospectively searched. Signalment, diagnostic, therapeutic, and outcome data were retrieved. Dogs were divided into six echocardiographic (dilated cardiomyopathy phenotype; hypertrophic cardiomyopathy phenotype; hypertrophic cardiomyopathy phenotype with systolic dysfunction; abnormal echogenicity only; endocarditis; and no echocardiographic abnormalities suggestive of MI), four electrocardiographic (abnormalities of impulse formation; abnormalities of impulse conduction; abnormalities of ventricular repolarization; and no electrocardiographic abnormalities suggestive of MI), and nine etiological (infective; inflammatory; neoplastic; metabolic; toxic; nutritional; immune-mediated; traumatic/mechanical; and unknown) categories. Statistical analysis was performed to compare cTnI values among different categories and analyze survival. ResultsOne hundred two dogs were included. The median cTnI value was 3.71 ng/mL (0.2–180 ng/mL). Echocardiographic and electrocardiographic abnormalities were documented in 86 of 102 and 89 of 102 dogs, respectively. Among echocardiographic and electrocardiographic categories, the dilated cardiomyopathy phenotype (n = 52) and abnormalities of impulse formation (n = 67) were overrepresented, respectively. Among dogs in which a suspected etiological trigger was identified (68/102), the infective category was overrepresented (n = 20). Among dogs belonging to different echocardiographic, electrocardiographic, and etiological categories, cTnI did not differ significantly. The median survival time was 603 days; only eight of 102 dogs died due to MI. ConclusionsDogs with MI often have an identifiable suspected trigger, show various echocardiographic and electrocardiographic abnormalities, and frequently survive to MI-related complications.

Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning

Continual learning (CL) has shown promising results and comparable performance to learning at once in a fully supervised manner. However, CL strategies typically require a large number of labeled samples, making their real-life deployment challenging. In this work, we focus on semi-supervised continual learning (SSCL), where the model progressively learns from partially labeled data with unknown categories. We provide a comprehensive analysis of SSCL and demonstrate that unreliable distributions of unlabeled data lead to unstable training and refinement of the progressing stages. This problem severely impacts the performance of SSCL. To address the limitations, we propose a novel approach called Dynamic Sub-Graph Distillation (DSGD) for semi-supervised continual learning, which leverages both semantic and structural information to achieve more stable knowledge distillation on unlabeled data and exhibit robustness against distribution bias. Firstly, we formalize a general model of structural distillation and design a dynamic graph construction for the continual learning progress. Next, we define a structure distillation vector and design a dynamic sub-graph distillation algorithm, which enables end-to-end training and adaptability to scale up tasks. The entire proposed method is adaptable to various CL methods and supervision settings. Finally, experiments conducted on three datasets CIFAR10, CIFAR100, and ImageNet-100, with varying supervision ratios, demonstrate the effectiveness of our proposed approach in mitigating the catastrophic forgetting problem in semi-supervised continual learning scenarios. Our code is available: https://github.com/fanyan0411/DSGD.

Entropic Open-Set Active Learning

Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data. Despite impressive performance in closed-set settings, most AL methods fail in real-world scenarios where the unlabeled data contains unknown categories. Recently, a few studies have attempted to tackle the AL problem for the open-set setting. However, these methods focus more on selecting known samples and do not efficiently utilize unknown samples obtained during AL rounds. In this work, we propose an Entropic Open-set AL (EOAL) framework which leverages both known and unknown distributions effectively to select informative samples during AL rounds. Specifically, our approach employs two different entropy scores. One measures the uncertainty of a sample with respect to the known-class distributions. The other measures the uncertainty of the sample with respect to the unknown-class distributions. By utilizing these two entropy scores we effectively separate the known and unknown samples from the unlabeled data resulting in better sampling. Through extensive experiments, we show that the proposed method outperforms existing state-of-the-art methods on CIFAR-10, CIFAR-100, and TinyImageNet datasets. Code is available at https://github.com/bardisafa/EOAL.

Revisiting Open-Set Panoptic Segmentation

In this paper, we focus on the open-set panoptic segmentation (OPS) task to circumvent the data explosion problem. Different from the close-set setting, OPS targets to detect both known and unknown categories, where the latter is not annotated during training. Different from existing work that only selects a few common categories as unknown ones, we move forward to the real-world scenario by considering the various tail categories (~1k). To this end, we first build a new dataset with long-tail distribution for the OPS task. Based on this dataset, we additionally add a new class type for unknown classes and re-define the training annotations to make the OPS definition more complete and reasonable. Moreover, we analyze the influence of several significant factors in the OPS task and explore the upper bound of performance on unknown classes with different settings. Furthermore, based on the analyses, we design an effective two-phase framework for the OPS task, including thing-agnostic map generation and unknown segment mining. We further adopt semi-supervised learning to improve the OPS performance. Experimental results on different datasets validate the effectiveness of our method.

Target Detection and Segmentation Technology for Zero-shot Learning

Zero-shot learning (ZSL) in the field of computer vision refers to enabling the model to recognize and understand categories that have not been encountered during the training phase. It is particularly critical for object detection and segmentation tasks, because these tasks require the model to have good generalization capabilities to unknown categories. Object detection requires the model to determine the location of the object, while segmentation further requires the precise demarcation of the object's boundaries. In ZSL research, knowledge representation and transfer are core issues. Researchers have tried to use semantic attributes as a knowledge bridge to connect categories seen during the training phase and categories not seen during the testing phase. These attributes may be color, shape, etc., but this method requires accurate attribute annotation, which is often not easy to achieve in practice. Therefore, researchers have begun to explore the use of non-visual information such as knowledge maps and text descriptions to enrich the recognition capabilities of models, but this also introduces the challenge of information integration and alignment. At present, ZSL has made certain progress in target detection and segmentation tasks, but there is still a significant gap compared with traditional supervised learning. This is mainly due to the limited ability of ZSL models to generalize to new categories. To this end, researchers have begun to explore combining ZSL with other technologies, such as generative adversarial networks (GANs) and reinforcement learning, to enhance the model's detection and segmentation capabilities for new categories. Future research needs to focus on several aspects. The first is how to design a more effective knowledge representation and transfer mechanism so that the model can better utilize existing knowledge. The second step is to develop new algorithms to improve the performance of ZSL in complex environments. In addition, research should focus on how to reduce the dependence on computing resources so that the ZSL method can run effectively in resource-limited environments. In summary, the research on target detection and segmentation technology of zero-shot learning is a cutting-edge topic in the field of computer vision. Despite the challenges, with the deepening of research, we expect these technologies to contribute to improving the generalization ability and intelligence level of computer vision systems.

Distributed fiber optic acoustic sensing system intrusion full event recognition based on 1-D MFEWnet

Proper detection of the full range of intrusion events is of paramount significance to distributed fiber optic sensing perimeter security systems. Traditional neural networks for intrusion event recognition are constrained by the training dataset, that is, they cannot detect intrusions outside of the training dataset. However, in real complex environments, the dataset by manually obtained is far fall short of encompassing all possible real-world data. This limitation can lead to inaccuracies of identification in the distributed fiber optic sensing system not being able to identify correctly, which causes immeasurable losses. In order to address the aforementioned issues, this paper presents a 1D MFEWnet model, which completes the effective differentiation of all datasets by means of a Multi-Feature branch 1-dimensional Convolution Neural Network, followed by fitting the activation vectors after the recognition of known datasets to a Weibull distribution, through the improved Euclidean distance tracing algorithm. This approach allows for the extraction and identification of additional intrusion signals while providing the ability to recognize and reject unknown interference events. In the experiments, a distributed fiber optic sensing system was established to collect event signals. For three known event categories, the highest recognition accuracy is up to 99.6%. After adding 2 unknown event categories randomly, the accuracy remained at a commendable 96.9%. This innovative methodology ensures the accuracy of target recognition under the introduction of all conceivable events and improves the robustness of the distributed fiber optic perimeter security system.

Towards Semi-Supervised Universal Graph Classification

Graph neural networks have pushed state-of-the-arts in graph classifications recently. Typically, these methods are studied within the context of supervised end-to-end training, which necessities copious task-specific labels. However, in real-world circumstances, labeled data could be limited, and there could be a massive corpus of unlabeled data, even from unknown classes as a complementary. Towards this end, we study the problem of semi-supervised universal graph classification, which not only identifies graph samples which do not belong to known classes, but also classifies the remaining samples into their respective classes. This problem is challenging due to a severe lack of labels and potential class shifts. In this paper, we propose a novel graph neural network framework named UGNN, which makes the best of unlabeled data from the subgraph perspective. To tackle class shifts, we estimate the certainty of unlabeled graphs using multiple subgraphs, which facilities the discovery of unlabeled data from unknown categories. Moreover, we construct semantic prototypes in the embedding space for both known and unknown categories and utilize posterior prototype assignments inferred from the Sinkhorn-Knopp algorithm to learn from abundant unlabeled graphs across different subgraph views. Extensive experiments on six datasets verify the effectiveness of UGNN in different settings.

Robust Data Association Against Detection Deficiency for Semantic SLAM

Robust and accurate object association is essential for precise 3D object landmark inference in semantic Simultaneous Localization and Mapping (SLAM), and yet remains challenging due to the detection deficiency caused by high miss detection rate, false alarm, occlusion and limited field-of-view, etc. The 2D location of an object is a crucial complementary cue to the appearance feature, especially in the case of associating objects across frames under large viewpoint changes. However, motion model or trajectory pattern based methods struggle to infer object motion reliably with a moving camera. In this paper, by exploiting the local projective warping consistency, a local homography based 2D motion inference method is proposed to sequentially estimate the object location along with uncertainty. By integrating the deep appearance feature and semantic information, an object association method, named HOA, which is robust to detection deficiency is proposed. Experimental evaluations suggest that the proposed motion prediction method is capable of maintaining a low cumulative error over a long duration, which enhances the object association performance in both accuracy and robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work aims to consistently associate 2D detection boxes corresponding to the same 3D object across images. In tasks of landmark-based navigation, collision avoidance, grasping and manipulation, objects in the task space are commonly simplified into 3D enveloping surfaces (e.g. cuboid or ellipsoid) by using 2D object detection boxes from multiple image views, and accurate data association is a prerequisite for precise enveloping surface reconstruction. This problem remains challenging considering the imperfect object detections, the appearance similarity of objects and the unpredictable trajectory of the moving camera. This work proposes a long-term reliable 2D location prediction algorithm that is capable of handling the complex motion of the target. Along with the appearance feature extracted by a retrain-free deep learning based model, this work proposes an object association method that can simultaneously deal with multiple objects with unknown object categories under the moving camera scenario.

PCNet: Leveraging Prototype Complementarity to Improve Prototype Affinity for Few-Shot Segmentation

With the advent of large-scale datasets, significant advancements have been made in image semantic segmentation. However, the annotation of these datasets necessitates substantial human and financial resources. Therefore, the focus of research has shifted towards few-shot semantic segmentation, which leverages a small number of labeled samples to effectively segment unknown categories. The current mainstream methods are to use the meta-learning framework to achieve model generalization, and the main challenges are as follows. (1) The trained model will be biased towards the seen class, so the model will misactivate the seen class when segmenting the unseen class, which makes it difficult to achieve the idealized class agnostic effect. (2) When the sample size is limited, there exists an intra-class gap between the provided support images and the query images, significantly impacting the model’s generalization capability. To solve the above two problems, we propose a network with prototype complementarity characteristics (PCNet). Specifically, we first generate a self-support query prototype based on the query image. Through the self-distillation, the query prototype and the support prototype perform feature complementary learning, which effectively reduces the influence of the intra-class gap on the model generalization. A standard semantic segmentation model is introduced to segment the seen classes during the training process to achieve accurate irrelevant class shielding. After that, we use the rough prediction map to extract its background prototype and shield the background in the query image by the background prototype. In this way, we obtain more accurate fine-grained segmentation results. The proposed method exhibits superiority in extensive experiments conducted on the PASCAL-5i and COCO-20i datasets. We achieve new state-of-the-art results in the few-shot semantic segmentation task, with an mIoU of 71.27% and 51.71% in the 5-shot setting, respectively. Comprehensive ablation experiments and visualization studies show that the proposed method has a significant effect on small-sample semantic segmentation.

Multi-Head Self-Attention-Enhanced Prototype Network with Contrastive–Center Loss for Few-Shot Relation Extraction

Few-shot relation extraction (FSRE) constitutes a critical task in natural language processing (NLP), involving learning relationship characteristics from limited instances to enable the accurate classification of new relations. The existing research primarily concentrates on using prototype networks for FSRE and enhancing their performance by incorporating external knowledge. However, these methods disregard the potential interactions among different prototype networks, and each prototype network can only learn and infer from its limited instances, which may limit the robustness and reliability of the prototype representations. To tackle the concerns outlined above, this paper introduces a novel prototype network called SACT (multi-head self-attention and contrastive-center loss), aimed at obtaining more comprehensive and precise interaction information from other prototype networks to bolster the reliability of the prototype network. Firstly, SACT employs a multi-head self-attention mechanism for capturing interaction information among different prototypes from traditional prototype networks, reducing the noise introduced by unknown categories with a small sample through information aggregation. Furthermore, SACT introduces a new loss function, the contrastive–center loss function, aimed at tightly clustering samples from a similar relationship category in the center of the feature space while dispersing samples from different relationship categories. Through extensive experiments on FSRE datasets, this paper demonstrates the outstanding performance of SACT, providing strong evidence for the effectiveness and practicality of SACT.

Reconstruction Guided Meta-Learning for Few Shot Open Set Recognition.

In many applications, we are constrained to learn classifiers from very limited data (few-shot classification). The task becomes even more challenging if it is also required to identify samples from unknown categories (open-set classification). Learning a good abstraction for a class with very few samples is extremely difficult, especially under open-set settings. As a result, open-set recognition has received limited attention in the few-shot setting. However, it is a critical task in many applications like environmental monitoring, where the number of labeled examples for each class is limited. Existing few-shot open-set recognition (FSOSR) methods rely on thresholding schemes, with some considering uniform probability for open-class samples. However, this approach is often inaccurate, especially for fine-grained categorization, and makes them highly sensitive to the choice of a threshold. To address these concerns, we propose Reconstructing Exemplar-based Few-shot Open-set ClaSsifier (ReFOCS). By using a novel exemplar reconstruction-based meta-learning strategy ReFOCS streamlines FSOSR eliminating the need for a carefully tuned threshold by learning to be self-aware of the openness of a sample. The exemplars, act as class representatives and can be either provided in the training dataset or estimated in the feature domain. By testing on a wide variety of datasets, we show ReFOCS to outperform multiple state-of-the-art methods.