A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection

• URL: http://arxiv.org/abs/2408.10940v1
• Date: Tue, 20 Aug 2024 15:29:56 GMT
• Title: A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection
• Authors: Vladislav Li, Georgios Tsoumplekas, Ilias Siniosoglou, Vasileios Argyriou, Anastasios Lytos, Eleftherios Fountoukidis, Panagiotis Sarigiannidis,
• Abstract summary: This paper examines both model performance and energy efficiency of custom data augmentations and automated data augmentation selection strategies. It is shown that in many cases, the performance gains of data augmentation strategies are overshadowed by their increased energy usage.
• Score: 5.434078645728145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current methods for low- and few-shot object detection have primarily focused on enhancing model performance for detecting objects. One common approach to achieve this is by combining model finetuning with data augmentation strategies. However, little attention has been given to the energy efficiency of these approaches in data-scarce regimes. This paper seeks to conduct a comprehensive empirical study that examines both model performance and energy efficiency of custom data augmentations and automated data augmentation selection strategies when combined with a lightweight object detector. The methods are evaluated in three different benchmark datasets in terms of their performance and energy consumption, and the Efficiency Factor is employed to gain insights into their effectiveness considering both performance and efficiency. Consequently, it is shown that in many cases, the performance gains of data augmentation strategies are overshadowed by their increased energy usage, necessitating the development of more energy efficient data augmentation strategies to address data scarcity.

Related papers

• 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)
• Parameter-Efficient Active Learning for Foundational models [7.799711162530711]
  Foundational vision transformer models have shown impressive few shot performance on many vision tasks. This research presents a novel investigation into the application of parameter efficient fine-tuning methods within an active learning (AL) framework.
  arXiv  Detail & Related papers  (2024-06-13T16:30:32Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings [6.611985866622974]
  This paper presents a finetuning approach to adapt standard object detection models to downstream tasks. Case study and evaluation of the energy demands of the developed models are presented. Finally, this paper introduces a novel way to quantify this trade-off through a customized Efficiency Factor metric.
  arXiv  Detail & Related papers  (2024-03-11T11:41:30Z)
• Leveraging the Power of Data Augmentation for Transformer-based Tracking [64.46371987827312]
  We propose two data augmentation methods customized for tracking. First, we optimize existing random cropping via a dynamic search radius mechanism and simulation for boundary samples. Second, we propose a token-level feature mixing augmentation strategy, which enables the model against challenges like background interference.
  arXiv  Detail & Related papers  (2023-09-15T09:18:54Z)
• Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances [0.16777183511743468]
  Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point. We introduce a novel DL model aimed at enhanced multi-label classification of NILM with improved computation and energy efficiency. Compared to the state-of-the-art, the proposed model has its energy consumption reduced by more than 23%.
  arXiv  Detail & Related papers  (2023-07-18T13:23:23Z)
• Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning [57.83232242068982]
  Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. It remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. This work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy.
  arXiv  Detail & Related papers  (2023-05-25T15:46:20Z)
• Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation [68.8204255655161]
  We provide the first study on semantic image segmentation and introduce two new approaches: textitSmartAugment and textitSmartSamplingAugment. SmartAugment uses Bayesian Optimization to search over a rich space of augmentation strategies and achieves a new state-of-the-art performance in all semantic segmentation tasks we consider. SmartSamplingAugment, a simple parameter-free approach with a fixed augmentation strategy competes in performance with the existing resource-intensive approaches and outperforms cheap state-of-the-art data augmentation methods.
  arXiv  Detail & Related papers  (2021-10-31T13:04:45Z)
• How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction [66.09605613944201]
  We quantitatively evaluate the effect of attention and Knowledge Graph on bag-level relation extraction (RE) We find that (1) higher attention accuracy may lead to worse performance as it may harm the model's ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns; and (3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior.
  arXiv  Detail & Related papers  (2021-07-26T09:38:28Z)
• Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations [2.1874189959020423]
  This paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. A novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images.
  arXiv  Detail & Related papers  (2020-09-14T12:04:30Z)

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.