{"title":"2024 Index IEEE Transactions on Artificial Intelligence Vol. 5","authors":"","doi":"10.1109/TAI.2025.3531741","DOIUrl":"https://doi.org/10.1109/TAI.2025.3531741","url":null,"abstract":"","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"1-93"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10847313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuemeng Hui;Zhunga Liu;Jiaxiang Liu;Zuowei Zhang;Longfei Wang
{"title":"Visual–Semantic Fuzzy Interaction Network for Zero-Shot Learning","authors":"Xuemeng Hui;Zhunga Liu;Jiaxiang Liu;Zuowei Zhang;Longfei Wang","doi":"10.1109/TAI.2024.3524955","DOIUrl":"https://doi.org/10.1109/TAI.2024.3524955","url":null,"abstract":"Zero-shot learning (ZSL) aims to recognize unseen class image objects using manually defined semantic knowledge corresponding to both seen and unseen images. The key of ZSL lies in building the interaction between precise image data and fuzzy semantic knowledge. The fuzziness is attributed to the difficulty in quantifying human knowledge. However, the existing ZSL methods ignore the inherent fuzziness of semantic knowledge and treat it as precise data during building the visual–semantic interaction. This is not good for transferring semantic knowledge from seen classes to unseen classes. In order to solve this problem, we propose a visual–semantic fuzzy interaction network (VSFIN) for ZSL. VSFIN utilize an effective encoder–decoder structure, including a semantic prototype encoder (SPE) and visual feature decoder (VFD). The SPE and VFD enable the visual features to interact with semantic knowledge via cross-attention. To achieve visual–semantic fuzzy interaction in SPE and VFD, we introduce the concept of membership function in fuzzy set theory and design a membership loss function. This loss function allows for a certain degree of imprecision in visual–semantic interaction, thereby enabling VSFIN to becomingly utilize the given semantic knowledge. Moreover, we introduce the concept of rank sum test and propose a distribution alignment loss to alleviate the bias towards seen classes. Extensive experiments on three widely used benchmarks have demonstrated that VSFIN outperforms current state-of-the-art methods under both conventional ZSL (CZSL) and generalized ZSL (GZSL) settings.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1345-1359"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yong Dai;Xiaopeng Hong;Yabin Wang;Zhiheng Ma;Dongmei Jiang;Yaowei Wang
{"title":"Prompt Customization for Continual Learning","authors":"Yong Dai;Xiaopeng Hong;Yabin Wang;Zhiheng Ma;Dongmei Jiang;Yaowei Wang","doi":"10.1109/TAI.2024.3524977","DOIUrl":"https://doi.org/10.1109/TAI.2024.3524977","url":null,"abstract":"Contemporary continual learning approaches typically select prompts from a pool, which function as supplementary inputs to a pretrained model. However, this strategy is hindered by the inherent noise of its selection approach when handling increasing tasks. In response to these challenges, we reformulate the prompting approach for continual learning and propose the prompt customization (PC) method. PC mainly comprises a prompt generation module (PGM) and a prompt modulation module (PMM). In contrast to conventional methods that employ hard prompt selection, PGM assigns different coefficients to prompts from a fixed-sized pool of prompts and generates tailored prompts. Moreover, PMM further modulates the prompts by adaptively assigning weights according to the correlations between input data and corresponding prompts. We evaluate our method on four benchmark datasets for three diverse settings, including the class, domain, and task-agnostic incremental learning tasks. Experimental results demonstrate consistent improvement (by up to 16.2%), yielded by the proposed method, over the state-of-the-art (SOTA) techniques. The code has been released online.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1373-1385"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Weakly Correlated Multimodal Domain Adaptation for Pattern Classification","authors":"Shuyue Wang;Zhunga Liu;Zuowei Zhang;Mohammed Bennamoun","doi":"10.1109/TAI.2024.3524976","DOIUrl":"https://doi.org/10.1109/TAI.2024.3524976","url":null,"abstract":"Multimodal domain adaptation (MMDA) aims to transfer knowledge across different domains that contain multimodal data. Current methods typically assume that both the source and target domains have paired multimodal data with the same modalities, allowing for direct knowledge transfer between corresponding types of data. However, in certain applications, the source domain benefits from advanced sensors and equipment, capturing more modalities than those available in the target domain. As a result, the information from the source modalities may not strongly align with that of the target modalities. This weak correlation hinders the effective utilization of all source data for the target domain. To address this challenge, we propose a weakly correlated multimodal domain adaptation (WCMMDA) method for pattern classification. WCMMDA is designed to acquire the modality-independent and category-related knowledge from the source domain, enabling the full utilization of available source modalities for effective knowledge transfer. Specifically, modality-invariant features are first extracted from the multimodal data to bridge the heterogeneity gap within each domain. Subsequently, domain-invariant features are further learned from these modality-invariant features to align the feature distributions across the source and target domains. A source-specific classifier is employed here, which predicts pseudo-labels for the target data and enables the feature extractor to explore category-related information in source features. Finally, a target-specific classifier is trained using the pseudolabeled target data, where highly reliable pseudolabels are selected based on confidence to improve classification performance. Extensive experiments are performed on the real-world multimodal datasets to demonstrate the superiority of WCMMDA.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 5","pages":"1360-1372"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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