Retrieval of Family Members Using Siamese Neural Network

• URL: http://arxiv.org/abs/2006.00174v1
• Date: Sat, 30 May 2020 04:32:16 GMT
• Title: Retrieval of Family Members Using Siamese Neural Network
• Authors: Jun Yu, Guochen Xie, Mengyan Li and Xinlong Hao
• Abstract summary: Retrieval of family members in the wild aims at finding family members of the given subject in the dataset. We propose our solution with deep Siamese neural network. Our solution can be divided into two parts: similarity and ranking.
• Score: 20.360475286657877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval of family members in the wild aims at finding family members of the given subject in the dataset, which is useful in finding the lost children and analyzing the kinship. However, due to the diversity in age, gender, pose and illumination of the collected data, this task is always challenging. To solve this problem, we propose our solution with deep Siamese neural network. Our solution can be divided into two parts: similarity computation and ranking. In training procedure, the Siamese network firstly takes two candidate images as input and produces two feature vectors. And then, the similarity between the two vectors is computed with several fully connected layers. While in inference procedure, we try another similarity computing method by dropping the followed several fully connected layers and directly computing the cosine similarity of the two feature vectors. After similarity computation, we use the ranking algorithm to merge the similarity scores with the same identity and output the ordered list according to their similarities. To gain further improvement, we try different combinations of backbones, training methods and similarity computing methods. Finally, we submit the best combination as our solution and our team(ustc-nelslip) obtains favorable result in the track3 of the RFIW2020 challenge with the first runner-up, which verifies the effectiveness of our method. Our code is available at: https://github.com/gniknoil/FG2020-kinship

Related papers

• SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition [15.537230343119875]
  This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities.
  arXiv  Detail & Related papers  (2023-09-21T13:06:30Z)
• Deep ensembles in bioimage segmentation [74.01883650587321]
  In this work, we propose an ensemble of convolutional neural networks (CNNs) In ensemble methods, many different models are trained and then used for classification, the ensemble aggregates the outputs of the single classifiers. The proposed ensemble is implemented by combining different backbone networks using the DeepLabV3+ and HarDNet environment.
  arXiv  Detail & Related papers  (2021-12-24T05:54:21Z)
• Learning Tracking Representations via Dual-Branch Fully Transformer Networks [82.21771581817937]
  We present a Siamese-like Dual-branch network based on solely Transformers for tracking. We extract a feature vector for each patch based on its matching results with others within an attention window. The method achieves better or comparable results as the best-performing methods.
  arXiv  Detail & Related papers  (2021-12-05T13:44:33Z)
• Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional data [0.0]
  We introduce a novel type of computationally efficient artificial neural network (ANN) called the rank similarity filter (RSF) RSFs can be used to transform and classify nonlinearly separable datasets with many data points and dimensions. Open-source code for RST, RSC and RSPC was written in Python using the popular scikit-learn framework to make it easily accessible.
  arXiv  Detail & Related papers  (2021-09-28T10:53:38Z)
• DeepI2P: Image-to-Point Cloud Registration via Deep Classification [71.3121124994105]
  DeepI2P is a novel approach for cross-modality registration between an image and a point cloud. Our method estimates the relative rigid transformation between the coordinate frames of the camera and Lidar. We circumvent the difficulty by converting the registration problem into a classification and inverse camera projection optimization problem.
  arXiv  Detail & Related papers  (2021-04-08T04:27:32Z)
• Solving Mixed Integer Programs Using Neural Networks [57.683491412480635]
  This paper applies learning to the two key sub-tasks of a MIP solver, generating a high-quality joint variable assignment, and bounding the gap in objective value between that assignment and an optimal one. Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP. We evaluate our approach on six diverse real-world datasets, including two Google production datasets and MIPLIB, by training separate neural networks on each.
  arXiv  Detail & Related papers  (2020-12-23T09:33:11Z)
• Multi-task Supervised Learning via Cross-learning [102.64082402388192]
  We consider a problem known as multi-task learning, consisting of fitting a set of regression functions intended for solving different tasks. In our novel formulation, we couple the parameters of these functions, so that they learn in their task specific domains while staying close to each other. This facilitates cross-fertilization in which data collected across different domains help improving the learning performance at each other task.
  arXiv  Detail & Related papers  (2020-10-24T21:35:57Z)
• Deep Fusion Siamese Network for Automatic Kinship Verification [20.360475286657877]
  We propose a deep siamese network to quantify the relative similarity between two individuals. Two deep siamese networks are integrated into a deep triplet network for tri-subject (i.e., father, mother and child) kinship verification.
  arXiv  Detail & Related papers  (2020-05-30T01:43:59Z)
• Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring [4.230510356675453]
  It is difficult for humans to determine a similarity score that is consistent across a large enough training set. We propose a hierarchical problem embedding algorithm, called Prob2Vec, that consists of abstraction and embedding steps. Prob2Vec 96.88% accuracy on a problem similarity test, in contrast to 75% from directly applying state-of-the-art sentence embedding methods.
  arXiv  Detail & Related papers  (2020-03-21T00:16:14Z)
• Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving [106.63673243937492]
  Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning. We frame the task of feedforward computation as solving a system of nonlinear equations. We then propose to find the solution using a Jacobi or Gauss-Seidel fixed-point method, as well as hybrid methods of both. Our method is guaranteed to give exactly the same values as the original feedforward computation with a reduced (or equal) number of parallelizable iterations, and hence reduced time given sufficient parallel computing power.
  arXiv  Detail & Related papers  (2020-02-10T10:11:31Z)

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.