Paper Link: https://openreview.net/forum?id=g_mpzwfbluI
Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)
Abstract: Matching model is essential for Image-Text Retrieval framework. Existing research usually train the model with a triplet loss and explore various strategy to retrieve hard negative sentences in the dataset. We argue that current retrieval-based negative sample construction approach is limited in the scale of the dataset thus fail to identify negative sample of high difficulty for every image. We propose our TAiloring neGative Sentences with Discrimination and Correction (TAGS-DC) to generate synthetic sentences automatically as negative samples. TAGS-DC is composed of masking and refilling to generate synthetic negative sentences with higher difficulty. To keep the difficulty during training, we mutually improve the retrieval and generation through parameter sharing. To further utilize fine-grained semantic of mismatch in the negative sentence, we propose two auxiliary tasks, namely word discrimination and word correction to improve the training. In experiments, we verify the effectiveness of our model on MS-COCO and Flickr30K compared with current state-of-the-art models and demonstrates its robustness and faithfulness in the further analysis.
Presentation Mode: This paper will be presented virtually
Virtual Presentation Timezone: UTC+8
Copyright Consent Signature (type Name Or NA If Not Transferrable): Zhihao Fan
Copyright Consent Name And Address: Fudan University, Shanghai, China