PP-SSL : Priority-Perception Self-Supervised Learning for Fine-Grained Recognition

• URL: http://arxiv.org/abs/2412.00134v1
• Date: Thu, 28 Nov 2024 15:47:41 GMT
• Title: PP-SSL : Priority-Perception Self-Supervised Learning for Fine-Grained Recognition
• Authors: ShuaiHeng Li, Qing Cai, Fan Zhang, Menghuan Zhang, Yangyang Shu, Zhi Liu, Huafeng Li, Lingqiao Liu,
• Abstract summary: Self-supervised learning is emerging in fine-grained visual recognition with promising results. Existing self-supervised learning methods are susceptible to irrelevant patterns in self-supervised tasks. We propose a novel Priority-Perception Self-Supervised Learning framework, denoted as PP-SSL.
• Score: 28.863121559446665
• License:
• Abstract: Self-supervised learning is emerging in fine-grained visual recognition with promising results. However, existing self-supervised learning methods are often susceptible to irrelevant patterns in self-supervised tasks and lack the capability to represent the subtle differences inherent in fine-grained visual recognition (FGVR), resulting in generally poorer performance. To address this, we propose a novel Priority-Perception Self-Supervised Learning framework, denoted as PP-SSL, which can effectively filter out irrelevant feature interference and extract more subtle discriminative features throughout the training process. Specifically, it composes of two main parts: the Anti-Interference Strategy (AIS) and the Image-Aided Distinction Module (IADM). In AIS, a fine-grained textual description corpus is established, and a knowledge distillation strategy is devised to guide the model in eliminating irrelevant features while enhancing the learning of more discriminative and high-quality features. IADM reveals that extracting GradCAM from the original image effectively reveals subtle differences between fine-grained categories. Compared to features extracted from intermediate or output layers, the original image retains more detail, allowing for a deeper exploration of the subtle distinctions among fine-grained classes. Extensive experimental results indicate that the PP-SSL significantly outperforms existing methods across various datasets, highlighting its effectiveness in fine-grained recognition tasks. Our code will be made publicly available upon publication.

Related papers

• An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition [49.45660055499103]
  Zero-shot human skeleton-based action recognition aims to construct a model that can recognize actions outside the categories seen during training. Previous research has focused on aligning sequences' visual and semantic spatial distributions. We introduce a new loss function sampling method to obtain a tight and robust representation.
  arXiv  Detail & Related papers  (2024-06-02T06:53:01Z)
• High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning [54.86882315023791]
  We propose an innovative approach called High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning (HDAFL) HDAFL utilizes multiple convolutional kernels to automatically learn discriminative regions highly correlated with attributes in images. We also introduce a Transformer-based attribute discrimination encoder to enhance the discriminative capability among attributes.
  arXiv  Detail & Related papers  (2024-04-07T13:17:47Z)
• RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition [78.97487780589574]
  Multimodal Large Language Models (MLLMs) excel at classifying fine-grained categories. This paper introduces a Retrieving And Ranking augmented method for MLLMs. Our proposed approach not only addresses the inherent limitations in fine-grained recognition but also preserves the model's comprehensive knowledge base.
  arXiv  Detail & Related papers  (2024-03-20T17:59:55Z)
• A Probabilistic Model Behind Self-Supervised Learning [53.64989127914936]
  In self-supervised learning (SSL), representations are learned via an auxiliary task without annotated labels. We present a generative latent variable model for self-supervised learning. We show that several families of discriminative SSL, including contrastive methods, induce a comparable distribution over representations.
  arXiv  Detail & Related papers  (2024-02-02T13:31:17Z)
• Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning [19.65026043141699]
  Generalized Zero-shot Learning (GZSL) has yielded remarkable performance by designing a series of unbiased visual-semantics mappings. We propose a simple yet effective Attribute-Aware Representation Rectification framework for GZSL, dubbed $mathbf(AR)2$.
  arXiv  Detail & Related papers  (2023-11-23T11:30:32Z)
• Enlarging Instance-specific and Class-specific Information for Open-set Action Recognition [47.69171542776917]
  We find that features with richer semantic diversity can significantly improve the open-set performance under the same uncertainty scores. A novel Prototypical Similarity Learning (PSL) framework is proposed to keep the instance variance within the same class to retain more IS information.
  arXiv  Detail & Related papers  (2023-03-25T04:07:36Z)
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems [61.11799513362704]
  We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
  arXiv  Detail & Related papers  (2023-03-03T02:07:40Z)
• DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning [37.48292304239107]
  We present a transformer-based end-to-end ZSL method named DUET. We develop a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images. We find that DUET can often achieve state-of-the-art performance, its components are effective and its predictions are interpretable.
  arXiv  Detail & Related papers  (2022-07-04T11:12:12Z)
• 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.