{"type":"ScholarlyArticle","title":"Incorporating Unlikely Negative Cues for Distinctive Image Captioning","authors":[{"name":"Zhengcong Fei","slug":"zhengcong-fei","given":"Zhengcong","family":"Fei","affiliation":""},{"name":"Junshi Huang","slug":"junshi-huang","given":"Junshi","family":"Huang","affiliation":""}],"abstract":"While recent neural image captioning models have shown great promise in terms of automatic metrics, they still struggle with generating generic sentences, which limits their use to only a handful of simple scenarios. On the other hand, negative training has been suggested as an effective way to prevent models from producing frequent yet meaningless sentences. However, when applied to image captioning, this approach may overlook low-frequency but generic and vague sentences, which can be problematic when dealing with diverse and changeable visual scenes. In this paper, we introduce a approach to improve image captioning by integrating negative knowledge that focuses on preventing the model from producing undesirable generic descriptions while addressing previous limitations. We accomplish this by training a negative teacher model that generates image-wise generic sentences with retrieval entropy-filtered data. Subsequently, the student model is required to maximize the distance with multi-level negative knowledge transferring for optimal guiding. Empirical results evaluated on MS COCO benchmark confirm that our plug-and-play framework incorporating unlikely negative knowledge leads to significant improvements in both accuracy and diversity, surpassing previous state-of-the-art methods for distinctive image captioning.","doi":"10.24963/ijcai.2023/83","url":"https://ijcai.org/proceedings/2023/83","pdf":"https://ijcai.org/proceedings/2023/0083.pdf","markdown":"https://ijcai.org/proceedings/2023/83.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/83","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":83,"pages":{"first":745,"last":753},"keywords":["Machine Learning: ML: Learning preferences or rankings","Computer Vision: CV: Vision and language"],"videos":[],"references":[{"position":1,"raw":"[Anderson et al., 2016] Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 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