A Deep Learning Framework for Automated Transfer Learning of Neural Networks

Balaiah, Thanasekhar; Jeyadoss, Timothy Jones Thomas; Thirumurugan, Sri Sainee; Ravi, R.

doi:10.1109/icoac48765.2019.246880

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…It not only keeps the model optimizing process fast and flexible but also strongly reduces the carbon footprint of the training the AI [83]. Moreover, recent literature [84] indicate that using pre-trained networks in many practical situations even lead to performance gains. Hence, we rather keep the model optimizing process fast and flexible, i.e.…”

Section: Discussionmentioning

confidence: 99%

Data-centric AI approach for automated wildflower monitoring

Schouten,

Michielsen,

Gravendeel

2024

Preprint

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Both researchers and policy makers are in need of standards and tools that help understanding and assessing natural capital. Wildflowers are a major component of our natural capital; they play an essential role in ecosystems, improve soil health, supply food and medicines, and curb climate change. In this paper, we present the Eindhoven Wildflower Dataset (EWD) as well as a PyTorch object detection model that is able to identify and count wildflowers. EWD, collected over two entire flowering seasons and expert annotated, contains 2002 top-view images of flowering plants captured ‘in the wild’ in five different landscape types (roadsides, urban green spaces, cropland, weed-rich grassland, marshland). It holds a total of 65571annotations for 160 species belonging to 31 different families of flowering plants and serves as a reference dataset for automating wildflower monitoring. To ensure consistent annotations, we define specific floral count units (largely based on inflorescences) and provide extensive annotation guidelines. With a 0.82 mAP (@IoU > 0.50) score the presented baseline model, trained with a balanced subset of EWD, is to the best of our knowledge superior in its class. Our approach empowers automated quantification of wildflower richness and abundance and encourages the development of standards for AI-based wildflower monitoring. The annotated EWD dataset is publicly available on the DataverseNL research data repository, and the code to train and run the baseline model is supplied as supplementary material.

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Section: Discussionmentioning

confidence: 99%

Data-centric AI approach for automated wildflower monitoring

Schouten,

Michielsen,

Gravendeel

2024

Preprint

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“…NetVLAD [3] inspired from VLAD is a CNN architecture used for image retrieval. [5] reduce the training time and provides an average improvement in accuracy. Using ACP is frequently in CBIR application thanks to his ability to reduce the descriptor dimension without losing its accuracy.…”

Section: Learning-based Featurementioning

confidence: 99%

A New CBIR Model Using Semantic Segmentation and Fast Spatial Binary Encoding

Ouni

Chateau

Royer

et al. 2022

Lecture Notes in Computer Science

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Content Based Image Retrieval(CBIR) is the task of finding similar images from a query one. Since the term similar means here "with the same semantic content", we propose to explore in this paper, a framework that uses Deep Neural Networks based semantic segmentation networks, coupled with a binary spatial encoding. Such simple representation has several relevant properties: 1) It takes advantage of the state of the art semantic segmentation networks and 2) the proposed binary encoding allows a Hamming distance that requests a very low computation budget resulting to a fast CBIR method. Several experiments achieved on public datasets show that our binary semantic signature leads to increase the CBIR accuracy and reduce the execution time. We study the performance of the proposed approach on six different public datasets:

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“…NetVLAD [3] inspired by VLAD is a CNN architecture used for image retrieval. [4] reduces the training time and provides an improvement in accuracy. Using ACP is frequent in the CBIR application thanks to its ability to reduce the descriptor dimension without losing its accuracy.…”

Section: Fig 2 Bag Of Visual Words Modelmentioning

confidence: 99%

Leveraging semantic segmentation for hybrid image retrieval methods

Ouni

Royer

Chevaldonné

et al. 2021

Neural Comput & Applic

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Content Based Image Retrieval (CBIR) is the task of finding images in a database that are the most similar to the input query based on its visual characteristics. Several methods from the state of the art based on visual methods (Bag of visual words, VLAD, ...) or recent deep leaning methods try to solve the CBIR problem. In particular, Deep learning is a new field and used for several vision applications including CBIR. But, even with the increase of the performance of deep learning algorithms, this problem is still a challenge in computer vision. In this work, we propose three different methodologies combining deep learning based semantic segmentation and visual features. We show experimentally that by exploiting semantic information in the CBIR context leads to an increase in the retrieval accuracy. We study the performance of the proposed approach on eight different datasets (Wang, Corel-10k, Corel-5k, GHIM-10K, MSRC V1, MSRC V2, Linnaeus, NUS-WIDE)

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A Deep Learning Framework for Automated Transfer Learning of Neural Networks

Cited by 6 publications

References 4 publications

Data-centric AI approach for automated wildflower monitoring

Data-centric AI approach for automated wildflower monitoring

A New CBIR Model Using Semantic Segmentation and Fast Spatial Binary Encoding

Leveraging semantic segmentation for hybrid image retrieval methods

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