Towards Latent Masked Image Modeling for Self-Supervised Visual Representation Learning

• URL: http://arxiv.org/abs/2407.15837v1
• Date: Mon, 22 Jul 2024 17:54:41 GMT
• Title: Towards Latent Masked Image Modeling for Self-Supervised Visual Representation Learning
• Authors: Yibing Wei, Abhinav Gupta, Pedro Morgado,
• Abstract summary: Masked Image Modeling (MIM) has emerged as a promising method for deriving visual representations from unlabeled image data by predicting missing pixels from masked portions of images. A promising yet unrealized framework is learning representations through masked reconstruction in latent space, combining the locality of MIM with the high-level targets. This study is among the first to thoroughly analyze and address the challenges of such framework, which we refer to as Latent MIM.
• Score: 18.424840375721303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Masked Image Modeling (MIM) has emerged as a promising method for deriving visual representations from unlabeled image data by predicting missing pixels from masked portions of images. It excels in region-aware learning and provides strong initializations for various tasks, but struggles to capture high-level semantics without further supervised fine-tuning, likely due to the low-level nature of its pixel reconstruction objective. A promising yet unrealized framework is learning representations through masked reconstruction in latent space, combining the locality of MIM with the high-level targets. However, this approach poses significant training challenges as the reconstruction targets are learned in conjunction with the model, potentially leading to trivial or suboptimal solutions.Our study is among the first to thoroughly analyze and address the challenges of such framework, which we refer to as Latent MIM. Through a series of carefully designed experiments and extensive analysis, we identify the source of these challenges, including representation collapsing for joint online/target optimization, learning objectives, the high region correlation in latent space and decoding conditioning. By sequentially addressing these issues, we demonstrate that Latent MIM can indeed learn high-level representations while retaining the benefits of MIM models.

Related papers

• MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling [64.09238330331195]
  We propose a novel Multi-Modal Auto-Regressive (MMAR) probabilistic modeling framework. Unlike discretization line of method, MMAR takes in continuous-valued image tokens to avoid information loss. We show that MMAR demonstrates much more superior performance than other joint multi-modal models.
  arXiv  Detail & Related papers  (2024-10-14T17:57:18Z)
• MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection [64.29452783056253]
  The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored. We propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities.
  arXiv  Detail & Related papers  (2024-09-15T13:08:59Z)
• Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training [87.69394953339238]
  Masked image modeling (MIM) has recently been introduced for fine-grained cross-modal alignment. We propose a semantics-enhanced cross-modal MIM framework (SemMIM) for vision-language representation learning.
  arXiv  Detail & Related papers  (2024-03-01T03:25:58Z)
• Fiducial Focus Augmentation for Facial Landmark Detection [4.433764381081446]
  We propose a novel image augmentation technique to enhance the model's understanding of facial structures. We employ a Siamese architecture-based training mechanism with a Deep Canonical Correlation Analysis (DCCA)-based loss. Our approach outperforms multiple state-of-the-art approaches across various benchmark datasets.
  arXiv  Detail & Related papers  (2024-02-23T01:34:00Z)
• PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning [35.39822183728463]
  We present a novel Prompt-IML framework for detecting tampered images. Humans tend to discern authenticity of an image based on semantic and high-frequency information. Our model can achieve better performance on eight typical fake image datasets.
  arXiv  Detail & Related papers  (2024-01-01T03:45:07Z)
• CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding [38.53988682814626]
  We propose a context-enhanced masked image modeling method (CtxMIM) for remote sensing image understanding. CtxMIM formulates original image patches as a reconstructive template and employs a Siamese framework to operate on two sets of image patches. With the simple and elegant design, CtxMIM encourages the pre-training model to learn object-level or pixel-level features on a large-scale dataset.
  arXiv  Detail & Related papers  (2023-09-28T18:04:43Z)
• PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling [83.67628239775878]
  Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT. This paper undertakes a fundamental analysis of MIM from the perspective of pixel reconstruction. We propose a remarkably simple and effective method, ourmethod, that entails two strategies.
  arXiv  Detail & Related papers  (2023-03-04T13:38:51Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding [74.33171794972688]
  We present algorithms to model phrase-object relevance by leveraging fine-grained visual representations and visually-aware language representations. Experiments conducted on the widely-adopted Flickr30k dataset show a significant improvement over existing weakly-supervised methods.
  arXiv  Detail & Related papers  (2020-10-12T00:43:52Z)

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.