{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cross-modal-alternating-learning-with-task","title":"Cross-Modal Alternating Learning with Task-Aware Representations for Continual Learning","arxiv_id":null,"date":"2023-12-07","proceeding":"IEEE TMM 2023 2023 12","authors":["Bin-Bin Gao"],"abstract":"Continual learning is a research field of artificial neural networks to simulate human lifelong learning ability. Although a surge of investigations has achieved considerable performance, most rely only on image modality for incremental image recognition tasks. In this paper, we propose a novel yet effective framework coined cross-modal Alternating Learning with Task-Aware representations (ALTA) to make good use of visual and linguistic modal information and achieve more effective continual learning. To do so, ALTA presents a cross-modal joint learning mechanism that leverages simultaneous learning of image and text representations to provide more effective supervision. And it mitigates forgetting by endowing task-aware representations with continual learning capability. Concurrently, considering the dilemma of stability and plasticity, ALTA proposes a cross-modal alternating learning strategy that alternately learns the task-aware cross-modal representations to match the image-text pairs between tasks better, further enhancing the ability of continual learning. We conduct extensive experiments under various popular image classification benchmarks to demonstrate that our approach achieves state-of-the-art performance. At the same time, systematic ablation studies and visualization analyses validate the effectiveness and rationality of our method. Our code for ALTA is available at \\url{https://github.com/vijaylee/ALTA}.","url_abs":"https://ieeexplore.ieee.org/document/10347466/metrics#metrics","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347466","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cross-modal-alternating-learning-with-task","repo_url":"https://github.com/vijaylee/ALTA","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":null,"task_name":"Incremental Learning on CIFAR100-B0(5steps of 20 classes)"},{"task_slug":"lifelong-learning","task_name":"Lifelong learning"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/continual-learning-on-cifar100-10-tasks","task":"Continual Learning","dataset":"Cifar100 (10 tasks)","model":"ALTA-ViTB/16","rank_in_archive_order":1,"of":5,"metrics":{"Average Accuracy":"92.85"},"uses_additional_data":false},{"leaderboard":"/sota/continual-learning-on-cifar100-10-tasks","task":"Continual Learning","dataset":"Cifar100 (10 tasks)","model":"ALTA-RN50x4","rank_in_archive_order":2,"of":5,"metrics":{"Average Accuracy":"84.91"},"uses_additional_data":false},{"leaderboard":"/sota/continual-learning-on-cifar100-10-tasks","task":"Continual Learning","dataset":"Cifar100 (10 tasks)","model":"ALTA-RN101","rank_in_archive_order":4,"of":5,"metrics":{"Average Accuracy":"84.77"},"uses_additional_data":false},{"leaderboard":"/sota/continual-learning-on-cifar100-10-tasks","task":"Continual Learning","dataset":"Cifar100 (10 tasks)","model":"ALTA-RN50","rank_in_archive_order":5,"of":5,"metrics":{"Average Accuracy":"83.87"},"uses_additional_data":false},{"leaderboard":"/sota/continual-learning-on-tiny-imagenet-10tasks","task":"Continual Learning","dataset":"Tiny-ImageNet (10tasks)","model":"ALTA-ViTB/16","rank_in_archive_order":1,"of":9,"metrics":{"Average Accuracy":"89.80"},"uses_additional_data":false},{"leaderboard":"/sota/continual-learning-on-tiny-imagenet-10tasks","task":"Continual Learning","dataset":"Tiny-ImageNet (10tasks)","model":"ALTA-RN50x4","rank_in_archive_order":2,"of":9,"metrics":{"Average Accuracy":"84.73"},"uses_additional_data":false},{"leaderboard":"/sota/continual-learning-on-tiny-imagenet-10tasks","task":"Continual Learning","dataset":"Tiny-ImageNet (10tasks)","model":"ALTA-RN101","rank_in_archive_order":3,"of":9,"metrics":{"Average Accuracy":"83.35"},"uses_additional_data":false},{"leaderboard":"/sota/continual-learning-on-tiny-imagenet-10tasks","task":"Continual Learning","dataset":"Tiny-ImageNet (10tasks)","model":"ALTA-RN50","rank_in_archive_order":4,"of":9,"metrics":{"Average Accuracy":"81.07"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}