Knowledge Perceived Multi-modal Pretraining in E-commerce

• URL: http://arxiv.org/abs/2109.00895v1
• Date: Fri, 20 Aug 2021 08:01:28 GMT
• Title: Knowledge Perceived Multi-modal Pretraining in E-commerce
• Authors: Yushan Zhu, Huaixiao Tou, Wen Zhang, Ganqiang Ye, Hui Chen, Ningyu Zhang and Huajun Chen
• Abstract summary: Current multi-modal pretraining methods for image and text modalities lack robustness in the face of modality-missing and modality-noise. We propose K3M, which introduces knowledge modality in multi-modal pretraining to correct the noise and supplement the missing of image and text modalities.
• Score: 12.012793707741562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we address multi-modal pretraining of product data in the field of E-commerce. Current multi-modal pretraining methods proposed for image and text modalities lack robustness in the face of modality-missing and modality-noise, which are two pervasive problems of multi-modal product data in real E-commerce scenarios. To this end, we propose a novel method, K3M, which introduces knowledge modality in multi-modal pretraining to correct the noise and supplement the missing of image and text modalities. The modal-encoding layer extracts the features of each modality. The modal-interaction layer is capable of effectively modeling the interaction of multiple modalities, where an initial-interactive feature fusion model is designed to maintain the independence of image modality and text modality, and a structure aggregation module is designed to fuse the information of image, text, and knowledge modalities. We pretrain K3M with three pretraining tasks, including masked object modeling (MOM), masked language modeling (MLM), and link prediction modeling (LPM). Experimental results on a real-world E-commerce dataset and a series of product-based downstream tasks demonstrate that K3M achieves significant improvements in performances than the baseline and state-of-the-art methods when modality-noise or modality-missing exists.

Related papers

• LLMs Can Evolve Continually on Modality for X-Modal Reasoning [62.2874638875554]
  Existing methods rely heavily on modal-specific pretraining and joint-modal tuning, leading to significant computational burdens when expanding to new modalities. We propose PathWeave, a flexible and scalable framework with modal-Path sWitching and ExpAnsion abilities. PathWeave performs comparably to state-of-the-art MLLMs while concurrently reducing parameter training burdens by 98.73%.
  arXiv  Detail & Related papers  (2024-10-26T13:19:57Z)
• Model Composition for Multimodal Large Language Models [71.5729418523411]
  We propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model. Our basic implementation, NaiveMC, demonstrates the effectiveness of this paradigm by reusing modality encoders and merging LLM parameters.
  arXiv  Detail & Related papers  (2024-02-20T06:38:10Z)
• Toward Robust Multimodal Learning using Multimodal Foundational Models [30.755818450393637]
  We propose TRML, Toward Robust Multimodal Learning using Multimodal Foundational Models. TRML employs generated virtual modalities to replace missing modalities. We also design a semantic matching learning module to align semantic spaces generated and missing modalities.
  arXiv  Detail & Related papers  (2024-01-20T04:46:43Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• What Makes for Robust Multi-Modal Models in the Face of Missing Modalities? [35.19295402483624]
  We model the scenarios of multi-modal models encountering missing modalities from an information-theoretic perspective. We introduce Uni-Modal Ensemble with Missing Modality Adaptation (UME-MMA) UME-MMA employs uni-modal pre-trained weights for the multi-modal model to enhance feature extraction and utilizes missing modality data augmentation techniques to better adapt to situations with missing modalities.
  arXiv  Detail & Related papers  (2023-10-10T07:47:57Z)
• UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning [86.91893533388628]
  This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC) UniDiff demonstrates versatility in both multi-modal understanding and generative tasks.
  arXiv  Detail & Related papers  (2023-06-01T15:39:38Z)
• Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis [19.07020276666615]
  We propose a novel framework named MultiModal Contrastive Learning (MMCL) for multimodal representation to capture intra- and inter-modality dynamics simultaneously. We also design two contrastive learning tasks, instance- and sentiment-based contrastive learning, to promote the process of prediction and learn more interactive information related to sentiment.
  arXiv  Detail & Related papers  (2022-10-26T08:24:15Z)
• i-Code: An Integrative and Composable Multimodal Learning Framework [99.56065789066027]
  i-Code is a self-supervised pretraining framework where users may flexibly combine the modalities of vision, speech, and language into unified and general-purpose vector representations. The entire system is pretrained end-to-end with new objectives including masked modality unit modeling and cross-modality contrastive learning. Experimental results demonstrate how i-Code can outperform state-of-the-art techniques on five video understanding tasks and the GLUE NLP benchmark, improving by as much as 11%.
  arXiv  Detail & Related papers  (2022-05-03T23:38:50Z)
• InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining [76.32065400614162]
  We propose a novel model, namely InterBERT (BERT for Interaction), which is the first model of our series of multimodal pretraining methods M6. The model owns strong capability of modeling interaction between the information flows of different modalities. We propose a large-scale dataset for multi-modal pretraining in Chinese, and we develop the Chinese InterBERT which is the first Chinese multi-modal pretrained model.
  arXiv  Detail & Related papers  (2020-03-30T03:13:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.