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, over the past two years. The majority of previous efforts based on intermodal transformer encoders focus on creating several proxy pre-training tasks (e.g., masked language modeling (MLM) and masked region modeling (MRM)) to learn joint intermodal representation. On the other hand, the cross-modal attention layers in the encoder attempt to merge different visual/textual features at the token level to understand the joint representation with massive interactions, which leads to high computational costs for world systems. real such as online cross-modal. recovery system.
Current research based on the dual-coder architecture uses an efficient computational framework with slight cross-modal interaction, yielding equivalent performance on visual language tasks by training on large-scale image-text pairings to overcome this constraint. . However, since the established cross-modal correlation depends on only one view for each modality, they attempt to develop cross-modal alignment via single-view contrastive learning. Indeed, the intramodal correlation they overlook has the potential to improve single-modal representation and contribute to the development of higher intermodal alignment. Moreover, there are often weak correlations in noisy image-text pairings explored on the Web with intrinsic visual/textual viewpoints, widening the cross-modal semantic gap.
They propose ERNIE-ViL 2.0, a multi-view contrastive learning framework for cross-modal retrieval, intended to learn a robust cross-modal representation by modeling cross-modal and intramodal correlations between distinct views. Unlike traditional single-view contrastive learning approaches, multi-view contrastive learning learns about both intramodal and crossmodal correlations. Similarly, CMC uses multi-view contrastive knowledge for visual representation learning, resulting in a more robust representation. Their approach creates many visual/textual viewpoints to enhance representations within and across modalities.
Multi-Perspective Contrastive Learning vs. Single-View Contrastive Learning Single-view contrastive learning relies solely on a single cross-modal association between a visual and textual perspective. Through the construction of many possible perspectives, multi-view contrastive learning could learn about many types of intra-modal and cross-modal correlations.
They specifically generate image-image pairings and text-text pairs for contrasting intramodal view pairs to improve representation with each modality. In addition to the intrinsic visual/textual views, they generate sequences of object labels as a single textual view to reduce the impacts of noisy multimodal data and facilitate the learning of vision-language alignment. They train an English model on 29 million publicly available datasets using the dual-encoder architecture and achieve competitive performance on cross-modal retrieval tasks. They increased the size of the training datasets to 1.5 billion Chinese image-text pairs, yielding significant gains over previous SOTA results on Chinese cross-modal retrieval.
Broadly, they divide their contributions into three categories:
1. We propose the first multi-view learning framework for cross-modal retrieval that uses multiple perspectives to produce invariant and resilient cross-modal representations.
2. They offer object tags as outstanding text views, bridging the semantic gap between image and text and making it easier to learn cross-modal alignment on large-scale noisy data.
3. Using only publicly available noisy datasets, create a credible and comparable benchmark for cross-modal recovery in English. Moreover, their model achieves SOTA performance on Chinese intermodal recovery after being trained on 1.5 billion Chinese image-text pairs.
Official implementations of many of the ERNIE family pre-training models covering topics such as language comprehension and generation and multimodal comprehension and 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. Please Don't Forget To Join Our ML Subreddit
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