MimCo: Masked Image Modeling Pre-training with Contrastive Teacher

• URL: http://arxiv.org/abs/2209.03063v2
• Date: Thu, 20 Apr 2023 07:41:05 GMT
• Title: MimCo: Masked Image Modeling Pre-training with Contrastive Teacher
• Authors: Qiang Zhou, Chaohui Yu, Hao Luo, Zhibin Wang, Hao Li
• Abstract summary: Masked image modeling (MIM) has received much attention in self-supervised learning (SSL) visualizations show that the learned representations are less separable, especially compared to those based on contrastive learning pre-training. We propose a novel and flexible pre-training framework, named MimCo, which combines MIM and contrastive learning through two-stage pre-training.
• Score: 14.413674270588023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent masked image modeling (MIM) has received much attention in self-supervised learning (SSL), which requires the target model to recover the masked part of the input image. Although MIM-based pre-training methods achieve new state-of-the-art performance when transferred to many downstream tasks, the visualizations show that the learned representations are less separable, especially compared to those based on contrastive learning pre-training. This inspires us to think whether the linear separability of MIM pre-trained representation can be further improved, thereby improving the pre-training performance. Since MIM and contrastive learning tend to utilize different data augmentations and training strategies, combining these two pretext tasks is not trivial. In this work, we propose a novel and flexible pre-training framework, named MimCo, which combines MIM and contrastive learning through two-stage pre-training. Specifically, MimCo takes a pre-trained contrastive learning model as the teacher model and is pre-trained with two types of learning targets: patch-level and image-level reconstruction losses. Extensive transfer experiments on downstream tasks demonstrate the superior performance of our MimCo pre-training framework. Taking ViT-S as an example, when using the pre-trained MoCov3-ViT-S as the teacher model, MimCo only needs 100 epochs of pre-training to achieve 82.53% top-1 finetuning accuracy on Imagenet-1K, which outperforms the state-of-the-art self-supervised learning counterparts.

Related papers

• Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate [118.37653302885607]
  We present the Modality Integration Rate (MIR), an effective, robust, and generalized metric to indicate the multi-modal pre-training quality of Large Vision Language Models (LVLMs) MIR is indicative about training data selection, training strategy schedule, and model architecture design to get better pre-training results.
  arXiv  Detail & Related papers  (2024-10-09T17:59:04Z)
• Membership Inference Attack Against Masked Image Modeling [29.699606401861818]
  Masked Image Modeling (MIM) has achieved significant success in the realm of self-supervised learning (SSL) for visual recognition. In this work, we take a different angle by studying the pre-training data privacy of MIM. We propose the first membership inference attack against image encoders pre-trained by MIM.
  arXiv  Detail & Related papers  (2024-08-13T11:34:28Z)
• Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning [12.5354658533836]
  Humans possess remarkable ability to accurately classify new, unseen images after being exposed to only a few examples. For artificial neural network models, determining the most relevant features for distinguishing between two images with limited samples presents a challenge. We propose an intra-task mutual attention method for few-shot learning, that involves splitting the support and query samples into patches.
  arXiv  Detail & Related papers  (2024-05-06T02:02:57Z)
• MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations [16.885965702357314]
  MIM-Refiner is a contrastive learning boost for pre-trained MIM models. We refine the features of MIM models from subpar to state-of-the-art, off-the-shelf features.
  arXiv  Detail & Related papers  (2024-02-15T16:46:16Z)
• Learning to Modulate pre-trained Models in RL [22.812215561012874]
  Fine-tuning a pre-trained model often suffers from catastrophic forgetting. Our study shows that with most fine-tuning approaches, the performance on pre-training tasks deteriorates significantly. We propose a novel method, Learning-to-Modulate (L2M), that avoids the degradation of learned skills by modulating the information flow of the frozen pre-trained model.
  arXiv  Detail & Related papers  (2023-06-26T17:53:05Z)
• Multi-Level Contrastive Learning for Dense Prediction Task [59.591755258395594]
  We present Multi-Level Contrastive Learning for Dense Prediction Task (MCL), an efficient self-supervised method for learning region-level feature representation for dense prediction tasks. Our method is motivated by the three key factors in detection: localization, scale consistency and recognition. Our method consistently outperforms the recent state-of-the-art methods on various datasets with significant margins.
  arXiv  Detail & Related papers  (2023-04-04T17:59:04Z)
• FastMIM: Expediting Masked Image Modeling Pre-training for Vision [65.47756720190155]
  FastMIM is a framework for pre-training vision backbones with low-resolution input images. It reconstructs Histograms of Oriented Gradients (HOG) feature instead of original RGB values of the input images. It can achieve 83.8%/84.1% top-1 accuracy on ImageNet-1K with ViT-B/Swin-B as backbones.
  arXiv  Detail & Related papers  (2022-12-13T14:09:32Z)
• Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders [64.03000385267339]
  Masked image modeling (MIM) has become a popular strategy for self-supervised learning(SSL) of visual representations with Vision Transformers. We present a simple SSL method, the Reconstruction-Consistent Masked Auto-Encoder (RC-MAE) by adding an EMA teacher to MAE. RC-MAE converges faster and requires less memory usage than state-of-the-art self-distillation methods during pre-training.
  arXiv  Detail & Related papers  (2022-10-05T08:08:55Z)
• Self-Supervised Pretraining Improves Self-Supervised Pretraining [83.1423204498361]
  Self-supervised pretraining requires expensive and lengthy computation, large amounts of data, and is sensitive to data augmentation. This paper explores Hierarchical PreTraining (HPT), which decreases convergence time and improves accuracy by initializing the pretraining process with an existing pretrained model. We show HPT converges up to 80x faster, improves accuracy across tasks, and improves the robustness of the self-supervised pretraining process to changes in the image augmentation policy or amount of pretraining data.
  arXiv  Detail & Related papers  (2021-03-23T17:37:51Z)
• Self-supervised Pre-training with Hard Examples Improves Visual Representations [110.23337264762512]
  Self-supervised pre-training (SSP) employs random image transformations to generate training data for visual representation learning. We first present a modeling framework that unifies existing SSP methods as learning to predict pseudo-labels. Then, we propose new data augmentation methods of generating training examples whose pseudo-labels are harder to predict than those generated via random image transformations.
  arXiv  Detail & Related papers  (2020-12-25T02:44:22Z)
• ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data [9.3935916515127]
  We introduce a new vision-supervised pre-trained model -- ImageBERT -- for image-text joint embedding. Our model is a Transformer-based model, which takes different modalities as input and models the relationship between them.
  arXiv  Detail & Related papers  (2020-01-22T11:35:58Z)

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.