A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species
Researchers can investigate many aspects of animal ecology through noninvasive photo–
identification. Photo–identification is becoming more efficient as matching individuals …
identification. Photo–identification is becoming more efficient as matching individuals …
Evit: Privacy-preserving image retrieval via encrypted vision transformer in cloud computing
Image retrieval systems help users to browse and search among extensive images in real
time. With the rise of cloud computing, retrieval tasks are usually outsourced to cloud …
time. With the rise of cloud computing, retrieval tasks are usually outsourced to cloud …
Universal image embedding: retaining and expanding knowledge with multi-domain fine-tuning
The overall purpose of this study is to propose a novel fine-tuning method for the CLIP
architecture that enables the retention of pre-existing knowledge from large datasets and the …
architecture that enables the retention of pre-existing knowledge from large datasets and the …
A fine-grained image classification algorithm based on self-supervised learning and multi-feature fusion of blood cells
N Jia, J Guo, Y Li, S Tang, L Xu, L Liu, J Xing - Scientific Reports, 2024 - nature.com
Leukemia is a prevalent and widespread blood disease, and its early diagnosis is crucial for
effective patient treatment. Diagnosing leukemia types heavily relies on pathologists' …
effective patient treatment. Diagnosing leukemia types heavily relies on pathologists' …
Large margin cotangent loss for deep similarity learning
Deep Convolutional Neural Networks (DCNN) models have become popular in feature
extraction tasks. One of the best approaches to effectively classify the features is to utilize the …
extraction tasks. One of the best approaches to effectively classify the features is to utilize the …
1st Place Solution in Google Universal Images Embedding
This paper presents the 1st place solution for the Google Universal Images Embedding
Competition on Kaggle. The highlighted part of our solution is based on 1) A novel way to …
Competition on Kaggle. The highlighted part of our solution is based on 1) A novel way to …
Scaleface: Uncertainty-aware deep metric learning
R Kail, K Fedyanin, N Muravev… - 2023 IEEE 10th …, 2023 - ieeexplore.ieee.org
The performance of modern deep learning-based systems dramatically depends on the
quality of input objects. For example, face recognition quality is lower for blurry or corrupted …
quality of input objects. For example, face recognition quality is lower for blurry or corrupted …
3rd place solution to google landmark recognition competition 2021
In this paper, we show our solution to the Google Landmark Recognition 2021 Competition.
Firstly, embeddings of images are extracted via various architectures (ie CNN-, Transformer …
Firstly, embeddings of images are extracted via various architectures (ie CNN-, Transformer …
6th Place Solution to Google Universal Image Embedding
This paper presents the 6th place solution to the Google Universal Image Embedding
competition on Kaggle. Our approach is based on the CLIP architecture, a powerful pre …
competition on Kaggle. Our approach is based on the CLIP architecture, a powerful pre …
Novel metric-learning methods for generalizable and discriminative few-shot image classification
M Méndez Ruiz - repositorio.tec.mx
Few-shot learning (FSL) is a challenging and relatively new technique that specializes in
problems where we have little amount of data. The goal of these methods is to classify …
problems where we have little amount of data. The goal of these methods is to classify …