Baidu Proposes ERNIE-VIL 2.0, a Multi-View Contrastive Learning Framework That Aims To Acquire A More Robust Cross-Modal Representation By Concurrently Building Intra-Modal And Inter-Modal Correlations Between Distinct Views

Vision-Language Pre-training (VLP) models have made significant progress on several cross-modal tasks, such as Visual Question Answering (VQA) and cross-modal retrieval, during the previous two years. The majority of prior efforts based on cross-modal transformer encoders concentrate on building several proxy pre-training tasks (e.g., Masked Language Modeling (MLM) and Masked Region Modeling (MRM)) to learn joint cross-modal representation. On the other hand, cross-modal attention layers in the encoder attempt to fuse different token-level visual/textual characteristics to understand the joint representation with massive interactions, resulting in high computing costs for real-world systems such as the online cross-modal retrieval system.

Current dual-encoder architecture-based research employs a compute-efficient framework with light cross-modal interaction, yielding equivalent performance on vision-language tasks by training on large-scale image-text pairings to solve this constraint. However, because the established inter-modal correlation only depends on a single view for each modality, they attempt to develop the cross-modal alignment via single-view contrastive learning. Indeed, the intra-modal correlation that they overlook has the potential to improve the single-modal representation and contribute to the development of a superior cross-modal alignment. Furthermore, there are frequently weak correlations in noisy web-crawled image-text pairings with intrinsic visual/textual viewpoints, expanding the cross-modal semantic gap.

They offer ERNIE-ViL 2.0, a multi-view contrastive learning framework for cross-modal retrieval, intending to learn robust cross-modal representation by modeling inter-modal and intra-modal correlations between distinct views. Unlike traditional single-view contrastive learning approaches, multi-view contrastive learning learns on both intra-modal and inter-modal correlations. Similarly, CMC employs multi-view contrastive knowledge for visual representation learning, resulting in a more robust representation. Their approach creates numerous visual/textual viewpoints to improve representations inside and across modalities.

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Source: https://arxiv.org/pdf/2209.15270v1.pdf

Contrastive learning with several perspectives vs. single-view contrastive learning Single-view contrastive learning relies solely on a single inter-modal association between a visual and textual perspective. Through the construction of numerous possible perspectives, multi-view contrastive learning might learn about many types of intra-modal and inter-modal correlations.

They specifically generate image-image pairings and text-text pairs for intra-modal contrastive view pairs to improve representation with each modality. In addition to the intrinsic visual/textual views, they generate object tag sequences as a unique textual view to lessen the impacts of noisy multi-modal data and facilitate vision-language alignment learning. They train an English model on 29M publically accessible datasets using the dual-encoder architecture and get competitive performance on cross-modal retrieval tasks. They increased the size of the training datasets to 1.5 billion Chinese image-text pairings, yielding considerable gains over earlier SOTA results on Chinese cross-modal retrieval.

Overall, they divide their contributions into three categories: 

1. We offer the first multi-view learning framework for cross-modal retrieval that uses several perspectives to produce view-invariant and resilient cross-modal representations. 

2. They offer object tags as exceptional textual views, thereby closing the semantics gap between image and text and making cross-modal alignment on large-scale noisy data easier to learn. 

3. Using only noisy publically available datasets, create a credible and comparable benchmark for English cross-modal retrieval. Furthermore, their model obtains SOTA performance on Chinese cross-modal recovery after being trained on 1.5 billion Chinese image-text pairings.

Official implementations for numerous ERNIE-family pre-training models covering subjects such as Language Understanding & Generation and Multimodal Understanding & Generation are available on GitHub.

This Article is written as a research summary article by Marktechpost Staff based on the research paper 'ERNIE-VIL 2.0: MULTI-VIEW CONTRASTIVE LEARNING FOR IMAGE-TEXT PRE-TRAINING'. All Credit For This Research Goes To Researchers on This Project. Check out the paper and github link.

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Aneesh Tickoo is a consulting intern at MarktechPost. He is currently pursuing his undergraduate degree in Data Science and Artificial Intelligence from the Indian Institute of Technology(IIT), Bhilai. He spends most of his time working on projects aimed at harnessing the power of machine learning. His research interest is image processing and is passionate about building solutions around it. He loves to connect with people and collaborate on interesting projects.