Leveraging Human Attention in Novel Object Captioning [conference paper]

Conference

30th International Joint Conference on Artificial Intelligence (IJCAI) - August 19 - 26, 2021

Authors

Xianyu Chen (Ph.D. student), Ming Jiang (postdoctoral associate), Qi Zhao (associate professor)

Abstract

Image captioning models depend on training with paired image-text corpora, which poses various challenges in describing images containing novel objects absent from the training data. While previous novel object captioning methods rely on external image taggers or object detectors to describe novel objects, we present the Attention based Novel Object Captioner (ANOC) that complements novel object captioners with human attention features that characterize generally important information independent of tasks. It introduces a gating mechanism that adaptively incorporates human attention with self-learned machine attention, with a Constrained Self-Critical Sequence Training method to address the exposure bias while maintaining constraints of novel object descriptions. Extensive experiments conducted on the no caps and Held-Out COCO datasets demonstrate that our method considerably outperforms the state of-the-art novel object captioners.

Link to full paper

Leveraging Human Attention in Novel Object Captioning

Keywords

computer vision, artificial intelligence

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