A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches

• URL: http://arxiv.org/abs/2409.01219v1
• Date: Mon, 2 Sep 2024 12:55:17 GMT
• Title: A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches
• Authors: Kim Jinwoo,
• Abstract summary: This review focuses on the roles of data augmentation and adversarial learning techniques in enhancing retrieval performance. Data augmentation enhances the model's generalization ability and robustness by generating more diverse training samples, simulating real-world variations, and reducing overfitting. adversarial attacks and defenses introduce perturbations during training to improve the model's robustness against potential attacks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Image retrieval is a crucial research topic in computer vision, with broad application prospects ranging from online product searches to security surveillance systems. In recent years, the accuracy and efficiency of image retrieval have significantly improved due to advancements in deep learning. However, existing methods still face numerous challenges, particularly in handling large-scale datasets, cross-domain retrieval, and image perturbations that can arise from real-world conditions such as variations in lighting, occlusion, and viewpoint. Data augmentation techniques and adversarial learning methods have been widely applied in the field of image retrieval to address these challenges. Data augmentation enhances the model's generalization ability and robustness by generating more diverse training samples, simulating real-world variations, and reducing overfitting. Meanwhile, adversarial attacks and defenses introduce perturbations during training to improve the model's robustness against potential attacks, ensuring reliability in practical applications. This review comprehensively summarizes the latest research advancements in image retrieval, with a particular focus on the roles of data augmentation and adversarial learning techniques in enhancing retrieval performance. Future directions and potential challenges are also discussed.

Related papers

• MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training [62.843316348659165]
  Deep learning-based image matching algorithms have dramatically outperformed humans in rapidly and accurately finding large amounts of correspondences. We propose a large-scale pre-training framework that utilizes synthetic cross-modal training signals to train models to recognize and match fundamental structures across images. Our key finding is that the matching model trained with our framework achieves remarkable generalizability across more than eight unseen cross-modality registration tasks.
  arXiv  Detail & Related papers  (2025-01-13T18:37:36Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models. In this paper, we investigate how detection performance varies across model backbones, types, and datasets. We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Proactive Schemes: A Survey of Adversarial Attacks for Social Good [13.213478193134701]
  Adversarial attacks in computer vision exploit the vulnerabilities of machine learning models by introducing subtle perturbations to input data. We examine the rise of proactive schemes-methods that encrypt input data using additional signals termed templates, to enhance the performance of deep learning models. The survey delves into the methodologies behind these proactive schemes, the encryption and learning processes, and their application to modern computer vision and natural language processing applications.
  arXiv  Detail & Related papers  (2024-09-24T22:31:56Z)
• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions [6.2719115566879236]
  Diffusion Models (DMs) have emerged as a powerful tool for image data augmentation. DMs generate realistic and diverse images by learning the underlying data distribution. Current challenges and future research directions in the field are discussed.
  arXiv  Detail & Related papers  (2024-07-04T18:06:48Z)
• Diffusion Deepfake [41.59597965760673]
  Recent progress in generative AI, primarily through diffusion models, presents significant challenges for real-world deepfake detection. The increased realism in image details, diverse content, and widespread accessibility to the general public complicates the identification of these sophisticated deepfakes. This paper introduces two extensive deepfake datasets generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2024-04-02T02:17:50Z)
• Data Augmentation in Human-Centric Vision [54.97327269866757]
  This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection. Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
  arXiv  Detail & Related papers  (2024-03-13T16:05:18Z)
• Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks [64.67735676127208]
  Text-to-image diffusion models have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. We introduce customized solutions by fully exploiting the aforementioned free attention masks.
  arXiv  Detail & Related papers  (2023-08-13T10:07:46Z)
• Causal Reasoning Meets Visual Representation Learning: A Prospective Study [117.08431221482638]
  Lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. Inspired by the strong inference ability of human-level agents, recent years have witnessed great effort in developing causal reasoning paradigms. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods.
  arXiv  Detail & Related papers  (2022-04-26T02:22:28Z)
• Image Data Augmentation for Deep Learning: A Survey [8.817690876855728]
  We systematically review different image data augmentation methods. We propose a taxonomy of reviewed methods and present the strengths and limitations of these methods. We also conduct extensive experiments with various data augmentation methods on three typical computer vision tasks.
  arXiv  Detail & Related papers  (2022-04-19T02:05:56Z)

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.