Fine-Grained Visual Textual Alignment for Cross-Modal Retrieval Using Transformer Encoders

By Nicola Messina, Giuseppe Amato, Andrea Esuli, Fabrizio Falchi, Claudio Gennaro, Stéphane Marchand-Maillet. 2020

169 citations recorded by the source corpus.

Despite the evolution of deep-learning-based visual-textual processing systems, precise multi-modal matching remains a challenging task. In this work, we tackle the task of cross-modal retrieval through image-sentence matching based on word-region alignments, using supervision only at the global image-sentence level. Specifically, we present a novel approach called Transformer Encoder Reasoning and Alignment Network (TERAN). TERAN enforces a fine-grained match between the underlying components of images and sentences (i.e., image regions and words, respectively) to preserve the informative richness of both modalities. TERAN obtains state-of-the-art results on the image retrieval task on both MS-COCO and Flickr30k datasets. Moreover, on MS-COCO, it also outperforms current approaches on the sentence retrieval task. Focusing on scalable cross-modal information retrieval, TERAN is designed to keep the visual and textual data pipelines well separated. Cross-attention links invalidate any chance to separately extract visual and textual features needed for the online search and the offline indexing steps in large-scale retrieval systems. In this respect, TERAN merges the information from the two domains only during the final alignment phase, immediately before the loss computation. We argue that the fine-grained alignments produced by TERAN pave the way toward the research for effective and efficient methods for large-scale cross-modal information retrieval. We compare the effectiveness of our approach against relevant state-of-the-art methods. On the MS-COCO 1K test set, we obtain an improvement of 5.7% and 3.5% respectively on the image and the sentence retrieval tasks on the Recall@1 metric. The code used for the experiments is publicly available on GitHub at https://github.com/mesnico/TERAN.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Retrieval & Search, Multimodal Learning

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Citations in the corpus

Combined Scaling for Zero-shot Transfer Learning (2021, neutral): “…Hodosh et al., 2013; Frome et al., 2013; Norouzi et al., 2013; Kiros et al., 2014; Socher et al., 2014; Akata et al., 2015b,a; Nam et al., 2017; Faghri et al., 2017; Li et al., 2019; Liu et al., 2019; Lu et al., 2019; Messina et al., 2020; Chen et al., 2020d; Huang et al., 2020; Chen et al., 2021).”
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (2021, background): “, leveraging object detectors, dense feature maps, or multi-attention layers) (Socher et al., 2014; Karpathy et al., 2014; Kiros et al.; Nam et al., 2017; Li et al., 2019; Messina et al., 2020; Chen et al., 2020a) have been proposed.”
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training (2021, background): “Most existing multi-modal pre-training models, especially those with the single-tower architecture [20, 33, 25, 36, 9, 11, 37, 22, 7, 14], take an assumption that there exists strong semantic correlation between the input imagetext pair.”
Combined Scaling for Open-Vocabulary Image Classiﬁcation (2022, unknown): “…Hodosh et al., 2013; Frome et al., 2013; Norouzi et al., 2013; Kiros et al., 2014; Socher et al., 2014; Akata et al., 2015b,a; Nam et al., 2017; Faghri et al., 2017; Li et al., 2019; Liu et al., 2019; Lu et al., 2019; Messina et al., 2020; Chen et al., 2020d; Huang et al., 2020; Chen et al., 2021).”