Few-shot learning brings the machine close to human thinking which enables fast learning with limited samples. Recent work considers local features to achieve contextual semantic complementation, while they are merely coarsened feature observations that can only extract insignificant label correlations. On the contrary, partial properties of few-shot examples significantly draw the implicit feature observations that can reveal the underlying label correlation of rare label classification. To fully explore the correlation between labels and partial features, this paper proposes a Part-Aware Correlation Network (PACNet) based on Partial Representation (PR) and Semantic Covariance Matrix (SCM). Specifically, we develop a partial representing module of an object that eliminates object-independent information and allows the model to focus on more distinctive parts. Furthermore, a semantic covariance measure function is redefined as a way to learn the semantic relationships of partial representations and to compute the partial similarity between the query sample and the support set. Experiments on three benchmark datasets consistently show that the proposed method outperforms the state-of-the-art counterparts, e.g. , on the PartImageNet dataset, the performance gains of up to 12% and 5.9% are observed for the 5-way 1-shot and 5-way 5-shot settings, respectively.

The label transition matrix has emerged as a widely accepted method for mitigating label noise in machine learning. In recent years, numerous studies have centered on leveraging deep neural networks to estimate the label transition matrix for individual instances within the context of instance-dependent noise. However, these methods suffer from low search efficiency due to the large space of feasible solutions. Behind this drawback, we have explored that the real murderer lies in the invalid class transitions, that is, the actual transition probability between certain classes is zero but is estimated to have a certain value. To mask the invalid class transitions , we introduced a human-cognition-assisted method with structural information from human cognition. Specifically, we introduce a structured transition matrix network ( STMN ) designed with an adversarial learning process to balance instance features and prior information from human cognition. The proposed method offers two advantages: 1) better estimation effectiveness is obtained by sparing the transition matrix and 2) better estimation accuracy is obtained with the assistance of human cognition. By exploiting these two advantages, our method parametrically estimates a sparse label transition matrix, effectively converting noisy labels into true labels. The efficiency and superiority of our proposed method are substantiated through comprehensive comparisons with state-of-the-art methods on three synthetic datasets and a real-world dataset. Our code will be available at https://github.com/WheatCao/STMN-Pytorch.

In recent years, growing needs for advanced security and traffic management have significantly heightened the prominence of the visible-infrared person re-identification community (VI-ReID), garnering considerable attention. A critical challenge in VI-ReID is the performance degradation attributable to label noise, an issue that becomes even more pronounced in cross-modal scenarios due to an increased likelihood of data confusion. While previous methods have achieved notable successes, they often overlook the complexities of instance-dependent and real-world noise, creating a disconnect from the practical applications of person re-identification. To bridge this gap, our research analyzes the primary sources of label noise in real-world settings, which include a) instantiated identities, b) blurry infrared images, and c) annotators’ errors. In response to these challenges, we develop a Robust Hybrid Loss function (RHL) that enables targeted recognition and retrieval optimization through a more fine-grained division of the noisy dataset. The proposed method categorises data into three sets: clean, obviously noisy, and indistinguishably noisy, with bespoke loss calculations for each category. The identification loss is structured to address the varied nature of these sets specifically. For the retrieval sub-task, we utilize an enhanced triplet loss, adept at handling noisy correspondences. Furthermore, to empirically validate our method, we have re-annotated a real-world dataset, SYSU-Real. Our experiments on SYSU-MM01 and RegDB, conducted under various noise ratios of random and instance-dependent label noise, demonstrate the generalized robustness and effectiveness of our proposed approach.

Graph pooling plays a crucial role in achieving effective local information aggregation. When dealing with graph, data in non-Euclidean space, a major challenge lies in the uncertainty of the number of nodes. Graph pooling with self-adaptivity can capture the optimal information region to aggregate shared information within this region. Existing methods rely on hard thresholds or parameter settings, making it hard to achieve real adaptiveness and often compromising structural integrity. Inspired by the special relationship between edges and nodes, we propose a real ADaptive Pooling (ADP) method, requiring no hyper-parameter tuning. Specifically, we obtain the graph’s main structure by filtering the most significant edges of each node. Subsequently, we utilize the naturally formed connected regions of n-edge graphs to define information regions. Finally, we integrate the features of nodes within these regions and establish new connections between the nodes. We hold these regions’ structural integrity and stability with triplet loss and information bottleneck compression loss. Our method achieves adaptive pooling while preserving graph structural features, and it has demonstrated state-of-the-art performance on 12 graph datasets in 4 types.

Infrared (IR) imaging is vital for perception under low-light and adverse weather conditions, yet its reliability is often undermined by ghost artifacts caused by specular reflections. These artifacts lead to false detections and hinder accurate scene understanding, particularly in safety-critical applications such as autonomous driving. Millimeter-wave (mmWave) radar offers motion-aware and geometry-consistent priors that hold significant potential for mitigating such artifacts. However, existing fusion methods struggle to reconcile the conflicting goals of object detection and ghost suppression. To address this challenge, we propose Co-D², a Collaborative Detection and De-ghosting framework that integrates radar-derived structural cues into infrared perception. The framework employs a Radar informed Cross-task Feature Separator (RCFS) to disentangle features for detection and restoration, coupled with a dual-branch architecture and an uncertainty-aware loss to balance the two tasks. We further establish the first benchmark for infrared ghost removal. Extensive experiments on real-world datasets demonstrate that Co-D² achieves superior performance in both detection and artifact suppression, highlighting its effectiveness for robust multimodal perception in complex environments.

Despite the recent accomplishments in joint infrared-visible imaging, the bimodal defocus blur (BDB) phenomenon received scant attention. Our analysis reveals that BDB is predominantly attributable to disparities in optical parameters between cameras, resulting in two primary challenges: incomplete single-modal information and difficulty in cross-modal information interaction. With regard to the former, the infrared modality is the primary victim, as the deblurring networks’ bias toward high-frequency results in erroneous low-frequency reconstruction (e.g., over-sharpening). In the latter case, the relative nature of the blur effect can lead to ambiguity in determining which modality’s information should be prioritized for guidance, and conflicts may arise between the clear components of the blurred image and the blurry components of the clear image. To address these issues, we propose the first de-bimodal defocus blur (DBDB) method, which consists of a low-frequency semantic hold (LSH) module with a pre-trained infrared model and a cross-modal complementary feature induction (CCFI) module driven by a max-min blur entropy loss. LSH is designed to ensure that the low-frequency information captured by the infrared modality does not contain any misleading data, while CCFI facilitates the acquisition of accurate information by means of adaptive adjustment and the loss function. The experimental results of deblurring and downstream tasks on two synthetic datasets demonstrate the superiority of our method.