{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T02:24:18Z","timestamp":1711419858770},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Vision-language foundation models have been incredibly successful in a wide range of downstream computer vision tasks using adaptation methods. However, due to the high cost of obtaining pre-training datasets, pairs with weak image-text correlation in the data exist in large numbers. We call them weak-paired samples. Due to the limitations of these weak-paired samples, the pre-training model are unable to mine all the knowledge from pre-training data. The existing adaptation methods do not consider the missing knowledge, which may lead to crucial task-related knowledge for the downstream tasks being ignored. To address this issue, we propose a new adaptation framework called Data Adaptive Traceback (DAT). Specifically, we utilize a zero-shot-based method to extract the most downstream task-related subset of the pre-training data to enable the downstream tasks. Furthermore, we adopt a pseudo-label-based semi-supervised technique to reuse the pre-training images and a vision-language contrastive learning method to address the confirmation bias issue in semi-supervised learning. We conduct extensive experiments that show our proposed DAT approach meaningfully improves various benchmark datasets\u2019 performance over traditional adaptation methods by simply.<\/jats:p>","DOI":"10.1609\/aaai.v38i5.28249","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T09:37:24Z","timestamp":1711359444000},"page":"4506-4514","source":"Crossref","is-referenced-by-count":0,"title":["Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification"],"prefix":"10.1609","volume":"38","author":[{"given":"Wenshuo","family":"Peng","sequence":"first","affiliation":[]},{"given":"Kaipeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Qiao","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2024,3,24]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/28249\/28493","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/28249\/28494","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/28249\/28493","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T09:37:24Z","timestamp":1711359444000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/28249"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,24]]},"references-count":0,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3,25]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v38i5.28249","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2024,3,24]]}}}