Learning Mechanism Research Articles

GluA2-containing AMPA receptors form a continuum of Ca2+-permeable channels.

Fast excitatory neurotransmission in the mammalian brain is mediated by cation-selective AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptors (AMPARs)1. AMPARs are critical for the learning and memory mechanisms of Hebbian plasticity2 and glutamatergic synapse homeostasis3, with recent work establishing that AMPAR missense mutations can cause autism and intellectual disability4-7. AMPARs have been grouped into two functionally distinct tetrameric assemblies based on the inclusion or exclusion of the GluA2 subunit that determines Ca2+ permeability through RNA editing8,9. GluA2-containing AMPARs are the most abundant in the central nervous system and considered to be Ca2+ impermeable10. Here we show this is not the case. Contrary to conventional understanding, GluA2-containing AMPARs form a continuum of polyamine-insensitive ion channels with varying degrees of Ca2+ permeability. Their ability to transport Ca2+ is shaped by the subunit composition of AMPAR tetramers as well as the spatial orientation of transmembrane AMPAR regulatory proteins and cornichon auxiliary subunits. Ca2+ crosses the ion-conduction pathway by docking to an extracellular binding site that helps funnel divalent ions into the pore selectivity filter. The dynamic range in Ca2+ permeability, however, arises because auxiliary subunits primarily modify the selectivity filter. Taken together, our work proposes a broader role for AMPARs in Ca2+ signalling in the mammalian brain and offers mechanistic insight into the pathogenic nature of missense mutations.

A corticostriatal learning mechanism linking excess striatal dopamine and auditory hallucinations.

1Auditory hallucinations are linked to elevated striatal dopamine, but their underlying computational mechanisms have been obscured by regional heterogeneity in striatal dopamine signaling. To address this, we developed a normative circuit model in which corticostriatal plasticity in the ventral striatum is modulated by reward prediction errors to drive reinforcement learning while that in the sensory-dorsal striatum is modulated by sensory prediction errors derived from internal belief to drive self-supervised learning. We then validate the key predictions of this model using dopamine recordings across striatal regions in mice, as well as human behavior in a hybrid learning task. Finally, we find that changes in learning resulting from optogenetic stimulation of the sensory striatum in mice and individual variability in hallucination proneness in humans are best explained by selectively enhancing dopamine levels in the model sensory striatum. These findings identify plasticity mechanisms underlying biased learning of sensory expectations as a biologically plausible link between excess dopamine and hallucinations.

Pain distribution can be determined by classical conditioning.

Chronic widespread pain (CWP)-as in many other clinical presentations-manifests in ongoing pain without identifiable structural cause, with pain that spreads over multiple body areas. The development and maintenance of symptoms may involve learning mechanisms. Ninety-four healthy volunteers participated in this study and were randomly distributed to 4 groups. In the classical conditioning combined with the verbal suggestion group, US- (small pain distribution) and US+ (large pain distribution) were paired with visual stimuli (CS+ and CS-), and participants were told about this association. In the verbal suggestion group, the conditioning was not performed, whereas in the classical conditioning-only group, learning was not combined with suggestion. In the control group, conditioning and suggestion did not take place. Ratings of perceived pain distribution were collected after each trial and ratings of pain intensity after each block of trials. During the testing phase, participants were exposed to electrocutaneous stimuli corresponding to only the small (US-) pain distribution. The results showed significant differences between CS+ and CS- pain distribution ratings across the experimental groups: conditioning + verbal suggestion (P < 0.01), conditioning-only group (P < 0.05), and verbal suggestion-only group (P < 0.05), but not in the control group (P > 0.05). Furthermore, significant differences in the perceived pain distribution were found between the control group and all experimental groups. This result supports our main hypothesis that the perceived pain distribution can be influenced by classical conditioning as well as verbal suggestion, although the effect is stronger when both are combined.

Enhanced lung cancer subtype classification using attention-integrated DeepCNN and radiomic features from CT images: a focus on feature reproducibility

ObjectiveThis study aims to assess a hybrid framework that combines radiomic features with deep learning and attention mechanisms to improve the accuracy of classifying lung cancer subtypes using CT images.Materials and methodsA dataset of 2725 lung cancer images was used, covering various subtypes: adenocarcinoma (552 images), SCC (380 images), small cell lung cancer (SCLC) (307 images), large cell carcinoma (215 images), and pulmonary carcinoid tumors (180 images). The images were extracted as 2D slices from 3D CT scans, with tumor-containing slices selected from scans obtained across five healthcare centers. The number of slices per patient varied between 7 and 30, depending on tumor visibility. CT images were preprocessed using standardization, cropping, and Gaussian smoothing to ensure consistency across scans from different imaging instruments used at the centers. Radiomic features, including first-order statistics (FOS), shape-based, and texture-based features, were extracted using the PyRadiomics library. A DeepCNN architecture, integrated with attention mechanisms in the second convolutional block, was used for deep feature extraction, focusing on diagnostically important regions. The dataset was split into training (60%), validation (20%), and testing (20%) sets. Various feature selection techniques, such as Non-negative Matrix Factorization (NMF) and Recursive Feature Elimination (RFE), were used, and multiple machines learning models, including XGBoost and Stacking, were evaluated using accuracy, sensitivity, and AUC metrics. The model’s reproducibility was validated using ICC analysis across different imaging conditions.ResultsThe hybrid model, which integrates DeepCNN with attention mechanisms, outperformed traditional methods. It achieved a testing accuracy of 92.47%, an AUC of 93.99%, and a sensitivity of 92.11%. XGBoost with NMF showed the best performance across all models, and the combination of radiomic and deep features improved classification further. Attention mechanisms played a key role in enhancing model performance by focusing on relevant tumor areas, reducing misclassification from irrelevant features. This also improved the performance of the 3D Autoencoder, boosting the AUC to 93.89% and accuracy to 93.24%.ConclusionsThis study shows that combining radiomic features with deep learning—especially when enhanced by attention mechanisms—creates a powerful and accurate framework for classifying lung cancer subtypes.Clinical trial number Not applicable.

Learning from eavesdropping on human-human encounters changes feeding location choice in horses (Equus Caballus)

When animals observe human signals, they may learn from them. Such learning from humans has been reported for intentional communication between humans with animals, but animals might also learn socially by observing unintentional information transfer when eavesdropping on humans-human encounters. In this study, 12 of 17 horses significantly changed their preference for a feeding location after observing approval in a human-human interaction there, and horses kept in social housing adapted in a higher percentage of trials to human-human demonstrations than those in individual housing. This indicates, for the first time, that some animals change their feeding strategies after eavesdropping on human-human demonstrations and that this adaptation may be dependent on social experience. As horses maintained the observed preference for a feeding location when the demonstrators were absent, we suggest that they learned by applying individual and social learning mechanisms. The horses social rank, age and sex did not affect their learning performance. However, particular demonstrators tended to have a stronger impact on the horses’ performance. Future research should further investigate the durability of this preference change in the absence of repeated demonstrations, and establish whether long-term social learning sets in. This would have important implications for unintentional long-term impacts of human interactions on interspecies communication.

Neuron Perception Inspired EEG Emotion Recognition With Parallel Contrastive Learning.

Considerable interindividual variability exists in electroencephalogram (EEG) signals, resulting in challenges for subject-independent emotion recognition tasks. Current research in cross-subject EEG emotion recognition has been insufficient in uncovering the shared neural underpinnings of affective processing in the human brain. To address this issue, we propose the parallel contrastive multisource domain adaptation (PCMDA) model, inspired by the neural representation mechanism in the ventral visual cortex. Our model employs a neuron-perception-inspired contrastive learning architecture for EEG-based emotion recognition in subject-independent scenarios. A two-stage alignment methodology is employed for the purpose of aligning numerous source domains with the target domain. This approach integrates a parallel contrastive loss (PCL) which simulates the self-supervised learning mechanism inherent in the neural representation of the human brain. Furthermore, a self-attention mechanism is integrated to extract emotion weights for each frequency band. Extensive experiments were conducted on three publicly available EEG emotion datasets, SJTU emotion EEG dataset (SEED), database for emotion analysis using physiological signals (DEAP), and finer-grained affective computing EEG dataset (FACED), to evaluate our proposed method. The results demonstrate that the PCMDA effectively utilizes the unique EEG features and frequency band information of each subject, leading to improved generalization across different subjects in comparison to other methods.

Learning Care Pathways Framework: A New Method to Implement, Learn, Replicate, and Scale up Care Pathways for and With the Patient

Background: Although care pathways are a response to the calls for a major change in health system redesign initiatives, very few articles have proposed an implementation method. Indeed, no method exists for large-scale projects of care pathways, as sets of interventions within health systems. Drawing on the systems thinking approach and the pragmatic sociology, we describe the implementation methodology of the Learning Care Pathways (LCP) framework, a method to implement, learn, replicate, and scale up care pathways for and with the patient. Methods: The LCP was conceptually developed through a series of literature reviews on key methodological concepts. As a comprehensive, theory-informed approach, the LCP emerged by linking implementation strategies, research methods, learning mechanisms and outcomes dimensions aimed at optimising care pathways. Results: Designed around 13 steps grouped into five phases, this framework provides implementation strategies, research methods and learning mechanisms, including levers for patient involvement. The pre-implementation phase enables the selection of the pilot project’s receiving environment and the design of the project. The implementation phase is designed to co-construct and implement an optimised care pathway based on a scientific analysis of the patient journey, the care pathway perceived by professionals, the care pathway from data and integrating knowledge from international clinical practice guidelines. The post implementation phase aims to demonstrate value creation and set up a learning cycle. The replication phase is designed to repeat the method locally to develop horizontal learning and to evaluate scalability. Finally, the scale up phase aims to repeat the method in other territories to accelerate knowledge creation and develop horizontal and vertical learning. Conclusion: This framework is of particular interest to policy-makers, healthcare managers, and researchers alike, and must be the subject of several experiments to conduct reproducible research that can lead to national Learning Health Systems (LHS).

PK-Judge: Enhancing IP Protection of Neural Network Models Using an Asymmetric Approach

This paper introduces PK-Judge, a novel neural network watermarking framework designed to enhance the intellectual property (IP) protection by incorporating an asymmetric cryptograp hic approach in the verification process. Inspired by the paradigm shift from HTTP to HTTPS in enhancing web security, this work integrates public key infrastructure (PKI) principles to establish a secure and verifiable watermarking system. Unlike symmetric approaches, PK-Judge employs a public key infrastructure (PKI) to decouple ownership validation from the extraction process, significantly increasing its resilience against adversarial attacks. Additionally, it incorporates a robust challenge-response mechanism to mitigate replay attacks and leverages error correction codes (ECC) to achieve an Effective Bit Error Rate (EBER) of zero, ensuring watermark integrity even under conditions such as fine-tuning, pruning, and overwriting. Furthermore, PK-Judge introduces a new requirement based on the principle of separation of privilege, setting a foundation for secure and scalable watermarking mechanisms in machine learning. By addressing these critical challenges, PK-Judge advances the state-of-the-art in neural network IP protection and integrity, paving the way for trust-based AI technologies that prioritize security and verifiability.

Habituation in Predictability-Modulations of Stimulus-Response Binding.

Acting intentionally requires the integration of perceptual with action information in a common representational format. In the action control literature, this integrated representation is often called event file and is measured in so-called stimulus-response binding effects. These effects allow us to measure the strength of this shared representation and the impact it can have on behavior. A well-established finding is that particular variables can modulate the size of binding effects - one recently discovered modulator is stimulus predictability: If perceptual information is perfectly predictable, stimulus-response binding effects diminish. Yet, the concrete mechanism of why predictability diminishes stimulus-response binding effects remained elusive so far. In the present study (N = 234), we compared two possible explanations for these modulation effects, namely habituation versus statistical learning. We found that it is unlikely that the predictability modulation is explained by habituation. Instead, we found evidence that is more in line with (but not exclusive to) learning of statistical regularities as an explanation. Our study thus adds to recent attempts to more closely relate learning mechanisms and action control.

Learning reshapes the hippocampal representation hierarchy

A key feature of biological and artificial neural networks is the progressive refinement of their neural representations with experience. In neuroscience, this fact has inspired several recent studies in sensory and motor systems. However, less is known about how higher associational cortical areas, such as the hippocampus, modify representations throughout the learning of complex tasks. Here, we focus on associative learning, a process that requires forming a connection between the representations of different variables for appropriate behavioral response. We trained rats in a space-context associative task and monitored hippocampal neural activity throughout the entire learning period, over several days. This allowed us to assess changes in the representations of context, movement direction, and position, as well as their relationship to behavior. We identified a hierarchical representational structure in the encoding of these three task variables that was preserved throughout learning. Nevertheless, we also observed changes at the lower levels of the hierarchy where context was encoded. These changes were local in neural activity space and restricted to physical positions where context identification was necessary for correct decision-making, supporting better context decoding and contextual code compression. Our results demonstrate that the hippocampal code not only accommodates hierarchical relationships between different variables but also enables efficient learning through minimal changes in neural activity space. Beyond the hippocampus, our work reveals a representation learning mechanism that might be implemented in other biological and artificial networks performing similar tasks.

When is a ranana a banana? Disentangling the mechanisms of error repair and word learning

ABSTRACT When faced with an ambiguous novel word such as ‘ranana’, how do listeners decide whether they heard a mispronunciation of a familiar target (‘banana’) or a label for an unfamiliar novel item? We examined this question by combining visual-world eye-tracking with an offline forced-choice judgment paradigm. In two studies, we show evidence that participants entertain repair and novel label interpretations of novel words that were created by editing a familiar target word in multiple phonetic features (Experiment 1) or a single phonetic feature (Experiment 2). Repair (‘ranana’ = a banana) and learning (‘ranana’ = a novel referent) were both common interpretation strategies, and learning was strongly associated with visual attention to the novel image after it was referred to in a sentence. This indicates that repair and learning are both valid strategies for understanding novel words that depend upon a set of similar mechanisms, and suggests that attention during listening is causally related to whether one learns or repairs.

An optimised deep learning model with an Atrous convolutional-based inception system for the sentiment analysis of customer online reviews

Nowadays, e-commerce sites like Amazon and Flipkart are widely available for purchasing and vending digital products, services and physical goods over the Internet. Also, using these platforms, a consumer or a user can share their opinions or suggestions about the sold product in terms of reviews and ratings, which helps to improve the product quality and services of the business firms for future consumers. This paper proposes a sentiment analysis of customer online reviews using an optimised deep learning model with a practical feature learning mechanism. Initially, the system performs dataset balancing using the fuzzy-c means (FCM) method to balance the collected dataset. After that, the preprocessing procedures are carried out to get data of good quality. Then, the system uses the Bidirectional Encoder Representations from Transformers (BERT) word embedding model to extract semantic information from the user data. After that, the feature extraction is done using the Atrous convolutional-based Inception Network (ACINet) model. Finally, the sentiment classification is performed using the Improved Bidirectional Gated Recurrent Unit (IBGRU) model. Our method performs superior to the existing model by achieving a maximum accuracy of 98.08%, 98.56% and 98.35% for the IMDB movie reviews, Amazon online reviews and Yelp restaurant review datasets.

Organizational Leaders' Views on Digital Health Competencies in Medical Education: Qualitative Semistructured Interview Study.

Digital technologies (DTs) have profoundly impacted health care delivery globally and are increasingly used in clinical practice. Despite this, there is a scarcity of guidelines for implementing training in digital health competencies (DHC) in medical schools, especially for clinical practice. A lack of sustained integration of DHC risks creating knowledge gaps due to a limited understanding of how DT should be used in health care. Furthermore, few studies have explored reasons for this lag, both within and beyond the medical school curriculum. Current frameworks to address these barriers are often specific to individual countries or schools and focus primarily on curriculum design and delivery. A comprehensive framework is therefore required to ensure consistent implementation of DHC across various contexts and times. This study aims to use Singapore as a case study and examine the perspectives of doctors in organizational leadership positions to identify and analyze the barriers to DHC implementation in the undergraduate curriculum of Singapore's medical schools. It also seeks to apply the Normalization Process Theory (NPT) to address these barriers and bridge the gap between health care systems and digital health education (DHE) training. Individual semistructured interviews were conducted with doctors in executive and organizational leadership roles. Participants were recruited through purposive sampling, and the data were interpreted using qualitative thematic analysis. A total of 33 doctors participated, 26 of whom are currently in organizational leadership roles and 7 of whom have previously held such positions. A total of 6 barriers were identified: bureaucratic inertia, lack of opportunities to pursue nontraditional career pathways, limited protective mechanisms for experiential learning and experimentation, lack of clear policy guidelines for clinical practice, insufficient integration between medical school education and clinical experience, and poor IT integration within the health care industry. These barriers are also present in other high-income countries experiencing health care digitalization, highlighting the need for a theoretical framework that broadens the generalizability of existing recommendations. Applying the NPT underscores the importance of addressing these barriers to effectively integrate DHC into the curriculum. The active involvement of multiple stakeholders and the incorporation of continuous feedback mechanisms are essential. Our proposed framework provides concrete, evidence-based, and step-by-step recommendations for implementation practice, supporting the introduction of DHC in undergraduate medical education.

Power Quality Disturbance Classification Strategy Based on Fast S-Transform and an Improved CNN-LSTM Hybrid Model

With the increasing complexity of power systems and the widespread application of power electronic equipment, power quality issues have become increasingly prominent, among which power quality disturbances are one of the key factors affecting the stable operation of power systems and the normal functioning of electrical equipment. Current research methods are still limited by feature extraction, insufficient model generalization ability, and strong data dependence. This paper proposes a power quality disturbance classification strategy based on the fast S-transform (FST) and an improved convolutional neural network–long short-term memory (CNN-LSTM) model to achieve accurate classification and identification of various power quality disturbances. Firstly, the FST is employed to process the power quality disturbance signals, enabling efficient analysis and feature extraction while effectively preserving the time–frequency characteristics of the signals and significantly reducing the computational burden. Secondly, to address the limitations of traditional CNN models in power quality disturbance classification, this paper introduces an improved CNN-LSTM hybrid classification model that integrates mechanism fusion. This model improves the classification performance and generalization ability for power quality disturbances by incorporating an enhanced sparrow search algorithm and learning mechanisms. Finally, the proposed strategy is experimentally validated using a large dataset of power quality disturbances. After analysis and comparison, the method proposed in this paper maintains an identification accuracy of over 97% even in strong noise environments when subjected to a single type of disturbance. Under complex conditions involving mixed disturbances of multiple types, the identification accuracy remains above 95%. Compared to existing methods, the proposed method achieves an improvement in identification accuracy by up to 3.2%. Additionally, its identification accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results demonstrate that the proposed strategy can accurately classify and identify various power quality disturbances, outperforming traditional methods in terms of classification accuracy and robustness.

Do autistic individuals show atypical performance in probabilistic learning? A comparison of cue-number, predictive strength, and prediction error

BackgroundAccording to recent models of autism, autistic individuals may find learning probabilistic cue-outcome associations more challenging than deterministic learning, though empirical evidence for this is mixed. Here we examined the mechanism of probabilistic learning more closely by comparing autistic and non-autistic adults on inferring a target cue from multiple cues or integrating multiple target cues and learning from associations with various predictive strengths.Methods52 autistic and 52 non-autistic participants completed three tasks: (i) single-cue probabilistic learning, in which they had to infer a single target cue from multiple cues to learn cue-outcome associations; (ii) multi-cue probabilistic learning, in which they had to learn associations of various predictive strengths via integration of multiple cues; and (iii) reinforcement learning, which required learning the contingencies of two stimuli with a probabilistic reinforcement schedule. Accuracy on the two probabilistic learning tasks was modelled separately using a binomial mixed effects model whereas computational modelling was performed on the reinforcement learning data to obtain a model parameter on prediction error integration (i.e., learning rate).ResultsNo group differences were found in the single-cue probabilistic learning task. Group differences were evident for the multi-cue probabilistic learning task for associations that are weakly predictive (between 40 and 60%) but not when they are strongly predictive (10–20% or 80–90%). Computational modelling on the reinforcement learning task revealed that, as a group, autistic individuals had a higher learning rate than non-autistic individuals.LimitationsDue to the online nature of the study, we could not confirm the diagnosis of our autistic sample. The autistic participants were likely to have typical intelligence, and so our findings may not be generalisable to the entire autistic population. The learning tasks are constrained by a relatively small number of trials, and so it is unclear whether group differences will still be seen when given more trials.ConclusionsAutistic adults showed similar performance as non-autistic adults in learning associations by inferring a single cue or integrating multiple cues when the predictive strength was strong. However, non-autistic adults outperformed autistic adults when the predictive strength was weak, but only in the later phase. Autistic individuals were also more likely to incorporate prediction errors during decision making, which may explain their atypical performance on the weakly predictive associations. Our findings have implications for understanding differences in social cognition, which is often noisy and weakly predictive, among autistic individuals.

SURAKSHA: Segmenting Ultrasound Images using Real-Time Attention and Knowledge-based Structured Hybrid Architecture for Possible Breast Cancer Diagnosis

Introduction: Breast cancer is one of the most common causes of death among women worldwide. Accurate and efficient diagnosis of breast cancer requires effective classification and segmentation of medical images. Though quite powerful, traditional deep learning mechanisms like Convolution Neural Networks (CNNs), Residual Neural Networks (ResNets), etc. are often computationally expensive, and resource-demanding. This calls for a Deep Learning approach to Constrained Quantization in Radiotherapy for Breast Cancer treatment by inheriting attention mechanisms. Objectives: The primary objective of this research is to develop an efficient deep learning model for breast cancer diagnosis that enhances segmentation of medical images. Methods: A novel deep learning architecture, SURAKSHA (Segmenting Ultrasound images using Real-time Attention and Knowledge-based Structured Hybrid Architecture) is proposed for possible Breast Cancer Diagnosis and identification of malignant tissues through the prediction of masks. Results: The proposed model outperforms the state-of-the-art models by achieving accuracy (validation) of 93.01% on a lightweight cancer dataset, that is between 0.139% to 11.083% improvements from the existing models. Further, the possible variation in performance with changes in the dimensions of the select patch is studied. Conclusions: The study presents an efficient deep learning-based segmentation to accurately detect malignant breast tissues, aiming to improve the effectiveness of radiotherapy.

Driven by edge intelligence: A biomechanical model-based study of mobile charging scheduling and privacy protection

With the wide application of electric vehicles, smart robots and Internet of Things (IoT) devices, efficient scheduling of mobile charging systems has become an important research direction in smart energy management. However, the traditional cloud computing architecture is difficult to meet the requirements of low latency, high reliability and privacy protection, and the existing scheduling strategies still have challenges in terms of energy optimization, task balancing and dynamic adaptability. To this end, this paper proposes an intelligent mobile charging scheduling method that integrates edge computing and biomechanical modeling, constructs a biomechanical-based charging demand modeling and energy consumption analysis framework, and combines bionic optimization algorithms to achieve efficient path planning. Meanwhile, an edge computing architecture is adopted to optimize resource scheduling, and a federated learning mechanism is designed to enhance cross-domain data processing capability. To safeguard user privacy, a multi-level privacy protection mechanism is proposed, combining differential privacy, homomorphic encryption and zero-knowledge proof to ensure data security. Experimental results show that the method outperforms traditional methods in terms of task response time, energy consumption optimization, load balancing and privacy security, and can significantly improve the charging scheduling efficiency and provide effective technical support for large-scale distributed charging networks. The research results provide a theoretical basis and engineering practice reference for the application of smart charging networks, edge intelligent computing and privacy protection technology.

Transcutaneous Vagus Nerve Stimulation Enhances Probabilistic Learning.

tVNS enhances various memory and learning mechanisms, but there is inconclusive evidence on whether probabilistic learning can be enhanced by tVNS. Here, we tested a simplified version of the probabilistic learning task with monetary rewards in a between-participants design with left and right-sided cymba conchae and tragus stimulation (compared to sham stimulation) in a sample of healthy individuals (n = 80, 64 women, on average 26.38 years old). tVNS enhances overall accuracy significantly (p = 4.09 x 10-04) and reduces response times (p = 1.1006 x 10-49) in the probabilistic learning phase. Reinforcement learning modelling of the data revealed that the tVNS group uses a riskier strategy, dedicates more time to stimulus encoding and motor processes and exhibits greater reward sensitivity relative to the sham group. The learning advantage for tVNS relative to sham persists (p = 0.005 for accuracy and p = 9.2501 × 10-27 for response times) during an immediate extinction phase with continued stimulation in which feedback and reward were omitted. Our observations are in line with the proposal that tVNS enhances reinforcement learning in healthy individuals. This suggests that tVNS may be useful in contexts where fast learning and learning persistence in the absence of a reward is an advantage, for example, in the case of learning new habits.

Examining children’s verb learning in the United States and Japan: Do comparisons help?

Examining children’s verb learning in the United States and Japan: Do comparisons help?

Improving Text-Based Person Retrieval by Excavating All-Round Information Beyond Color.

Text-based person retrieval is the process of searching a massive visual resource library for images of a particular pedestrian, based on a textual query. Existing approaches often suffer from a problem of color (CLR) over-reliance, which can result in a suboptimal person retrieval performance by distracting the model from other important visual cues such as texture and structure information. To handle this problem, we propose a novel framework to Excavate All-round Information Beyond Color for the task of text-based person retrieval, which is therefore termed EAIBC. The EAIBC architecture includes four branches, namely an RGB branch, a grayscale (GRS) branch, a high-frequency (HFQ) branch, and a CLR branch. Furthermore, we introduce a mutual learning (ML) mechanism to facilitate communication and learning among the branches, enabling them to take full advantage of all-round information in an effective and balanced manner. We evaluate the proposed method on three benchmark datasets, including CUHK-PEDES, ICFG-PEDES, and RSTPReid. The experimental results demonstrate that EAIBC significantly outperforms existing methods and achieves state-of-the-art (SOTA) performance in supervised, weakly supervised, and cross-domain settings.