Image-Text Matching with Multi-View Attention

Existing two-stream models for image-text matching show good performance while ensuring retrieval speed and have received extensive attention from industry and academia. These methods use a single representation to encode image and text separately and get a matching score with cosine similarity or the inner product of vectors. However, the performance of the two-stream model is often sub-optimal. On the one hand, a single representation is challenging to cover complex content comprehensively. On the other hand, in this framework of lack of interaction, it is challenging to match multiple meanings which leads to information being ignored. To address the problems mentioned above and facilitate the performance of the two-stream model, we propose a multi-view attention approach for two-stream image-text matching MVAM (\textbf{M}ulti-\textbf{V}iew \textbf{A}ttention \textbf{M}odel). It first learns multiple image and text representations by diverse attention heads with different view codes. And then concatenate these representations into one for matching. A diversity objective is also used to promote diversity between attention heads. With this method, models are able to encode images and text from different views and attend to more key points. So we can get representations that contain more information. When doing retrieval tasks, the matching scores between images and texts can be calculated from different aspects, leading to better matching performance. Experiment results on MSCOCO and Flickr30K show that our proposed model brings improvements over existing models. Further case studies show that different attention heads can focus on different contents and finally obtain a more comprehensive representation.

MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning

Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge. Several studies have leveraged text retrieval to augment the models' commonsense ability. Unlike text, images capture commonsense information inherently but little effort has been paid to effectively utilize them. In this work, we propose a novel Multi-mOdal REtrieval (MORE) augmentation framework, to leverage both text and images to enhance the commonsense ability of language models. Extensive experiments on the Common-Gen task have demonstrated the efficacy of MORE based on the pre-trained models of both single and multiple modalities.

WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training

Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction between image-text pairs, by assuming that there exists strong semantic correlation between the text and image modalities. Since this strong assumption is often invalid in real-world scenarios, we choose to implicitly model the cross-modal correlation for large-scale multi-modal pre-training, which is the focus of the Chinese project `WenLan' led by our team. Specifically, with the weak correlation assumption over image-text pairs, we propose a two-tower pre-training model called BriVL within the cross-modal contrastive learning framework. Unlike OpenAI CLIP that adopts a simple contrastive learning method, we devise a more advanced algorithm by adapting the latest method MoCo into the cross-modal scenario. By building a large queue-based dictionary, our BriVL can incorporate more negative samples in limited GPU resources. We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model. Extensive experiments demonstrate that the pre-trained BriVL model outperforms both UNITER and OpenAI CLIP on various downstream tasks.

Beyond Language: Learning Commonsense from Images for Reasoning

This paper proposes a novel approach to learn commonsense from images, instead of limited raw texts or costly constructed knowledge bases, for the commonsense reasoning problem in NLP. Our motivation comes from the fact that an image is worth a thousand words, where richer scene information could be leveraged to help distill the commonsense knowledge, which is often hidden in languages. Our approach, namely Loire, consists of two stages. In the first stage, a bi-modal sequence-to-sequence approach is utilized to conduct the scene layout generation task, based on a text representation model ViBERT. In this way, the required visual scene knowledge, such as spatial relations, will be encoded in ViBERT by the supervised learning process with some bi-modal data like COCO. Then ViBERT is concatenated with a pre-trained language model to perform the downstream commonsense reasoning tasks. Experimental results on two commonsense reasoning problems, i.e. commonsense question answering and pronoun resolution, demonstrate that Loire outperforms traditional language-based methods. We also give some case studies to show what knowledge is learned from images and explain how the generated scene layout helps the commonsense reasoning process.