Generative Negative Text Replay for Continual Vision-Language Pretraining

• URL: http://arxiv.org/abs/2210.17322v1
• Date: Mon, 31 Oct 2022 13:42:21 GMT
• Title: Generative Negative Text Replay for Continual Vision-Language Pretraining
• Authors: Shipeng Yan, Lanqing Hong, Hang Xu, Jianhua Han, Tinne Tuytelaars, Zhenguo Li, Xuming He
• Abstract summary: Vision-language pre-training has attracted increasing attention recently. Massive data are usually collected in a streaming fashion. We propose a multi-modal knowledge distillation between images and texts to align the instance-wise prediction between old and new models.
• Score: 95.2784858069843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language pre-training (VLP) has attracted increasing attention recently. With a large amount of image-text pairs, VLP models trained with contrastive loss have achieved impressive performance in various tasks, especially the zero-shot generalization on downstream datasets. In practical applications, however, massive data are usually collected in a streaming fashion, requiring VLP models to continuously integrate novel knowledge from incoming data and retain learned knowledge. In this work, we focus on learning a VLP model with sequential chunks of image-text pair data. To tackle the catastrophic forgetting issue in this multi-modal continual learning setting, we first introduce pseudo text replay that generates hard negative texts conditioned on the training images in memory, which not only better preserves learned knowledge but also improves the diversity of negative samples in the contrastive loss. Moreover, we propose multi-modal knowledge distillation between images and texts to align the instance-wise prediction between old and new models. We incrementally pre-train our model on both the instance and class incremental splits of the Conceptual Caption dataset, and evaluate the model on zero-shot image classification and image-text retrieval tasks. Our method consistently outperforms the existing baselines with a large margin, which demonstrates its superiority. Notably, we realize an average performance boost of $4.60\%$ on image-classification downstream datasets for the class incremental split.

Related papers

• ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation Model [19.915033191502328]
  This work aims to develop a retinal foundation model, called ViLReF, by pre-training on a paired dataset comprising 451,956 retinal images and corresponding diagnostic text reports. In our vision-language pre-training strategy, we leverage expert knowledge to facilitate the extraction of labels. We employ a batch expansion module with dynamic memory queues, maintained by momentum encoders, to supply extra samples and compensate for the vacancies caused by eliminating false negatives.
  arXiv  Detail & Related papers  (2024-08-20T14:27:03Z)
• Vision Model Pre-training on Interleaved Image-Text Data via Latent Compression Learning [78.19528555505961]
  We propose a novel vision model pre-training method called Latent Compression Learning (LCL) for interleaved image-text data. The training objective can be decomposed into two basic tasks: 1) contrastive learning between visual representation and preceding context, and 2) generating subsequent text based on visual representation. Our experiments demonstrate that our method not only matches the performance of CLIP on paired pre-training datasets, but can also leverage interleaved pre-training data.
  arXiv  Detail & Related papers  (2024-06-11T17:59:35Z)
• Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior [14.232144691524528]
  Recent Vision-Language Pretrained models have become the backbone for many downstream tasks. MLE training can lead the context vector to over-fit dominant image features in the training data. This paper presents a Bayesian-based framework of prompt learning, which could alleviate the overfitting issues on few-shot learning application.
  arXiv  Detail & Related papers  (2024-01-09T10:15:59Z)
• ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious Correlations [43.323791505213634]
  ASPIRE (Language-guided Data Augmentation for SPurIous correlation REmoval) is a solution for supplementing the training dataset with images without spurious features. It can generate non-spurious images without requiring any group labeling or existing non-spurious images in the training set. It improves the worst-group classification accuracy of prior methods by 1% - 38%.
  arXiv  Detail & Related papers  (2023-08-19T20:18:15Z)
• Multimodal Data Augmentation for Image Captioning using Diffusion Models [12.221685807426264]
  We propose a data augmentation method, leveraging a text-to-image model called Stable Diffusion, to expand the training set. Experiments on the MS COCO dataset demonstrate the advantages of our approach over several benchmark methods. Further improvement regarding the training efficiency and effectiveness can be obtained after intentionally filtering the generated data.
  arXiv  Detail & Related papers  (2023-05-03T01:57:33Z)
• Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing [53.89917396428747]
  Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. We explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
  arXiv  Detail & Related papers  (2023-01-11T16:35:33Z)
• Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone [170.85076677740292]
  We present FIBER (Fusion-In-the-Backbone-basedER), a new model architecture for vision-language (VL) pre-training. Instead of having dedicated transformer layers for fusion after the uni-modal backbones, FIBER pushes multimodal fusion deep into the model. We conduct comprehensive experiments on a wide range of VL tasks, ranging from VQA, image captioning, and retrieval, to phrase grounding, referring expression comprehension, and object detection.
  arXiv  Detail & Related papers  (2022-06-15T16:41:29Z)
• DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting [91.56988987393483]
  We present a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP. Specifically, we convert the original image-text matching problem in CLIP to a pixel-text matching problem and use the pixel-text score maps to guide the learning of dense prediction models. Our method is model-agnostic, which can be applied to arbitrary dense prediction systems and various pre-trained visual backbones.
  arXiv  Detail & Related papers  (2021-12-02T18:59:32Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)

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.