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Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning
Computer Vision and Pattern Recognition · Submitted 24 Apr 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The results demonstrate that RFE is competitive or superior to the state-of-the-art in diverse evaluated scenarios.
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The abstract, sentence by sentence
Impressive advances in acquisition and sharing technologies have made the growth of multimedia collections and their applications almost unlimited. However, the opposite is true for the availability of labeled data, which is needed for supervised training, since such data is often expensive and time-consuming to obtain. While there is a pressing need for the development of effective retrieval and classification methods, the difficulties faced by supervised approaches highlight the relevance of methods capable of operating with few or no labeled data. In this work, we propose a novel manifold learning algorithm named Rank Flow Embedding (RFE) for unsupervised and semi-supervised scenarios. The proposed method is based on ideas recently exploited by manifold learning approaches, which include hypergraphs, Cartesian products, and connected components. The algorithm computes context-sensitive embeddings, which are refined following a rank-based processing flow, while complementary contextual information is incorporated. The generated embeddings can be exploited for more effective unsupervised retrieval or semi-supervised classification based on Graph Convolutional Networks. Experimental results were conducted on 10 different collections. Various features were considered, including the ones obtained with recent Convolutional Neural Networks (CNN) and Vision Transformer (ViT) models. High effective results demonstrate the effectiveness of the proposed method on different tasks: unsupervised image retrieval, semi-supervised classification, and person Re-ID. The results demonstrate that RFE is competitive or superior to the state-of-the-art in diverse evaluated scenarios.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Impressive advances in acquisition and sharing technologies have made the growth of multimedia collections and their applications almost unlimited. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | However, the opposite is true for the availability of labeled data, which is needed for supervised training, since such data is often expensive and time-consuming to obtain. | background 0.67 | 0.06 | 0.02 | asserted 0.99 |
| s2 | While there is a pressing need for the development of effective retrieval and classification methods, the difficulties faced by supervised approaches highlight the relevance of methods capable of operating with few or no labeled data. | background 0.67 | 0.04 | 0.03 | asserted 0.97 |
| s3 | In this work, we propose a novel manifold learning algorithm named Rank Flow Embedding (RFE) for unsupervised and semi-supervised scenarios. | contribution 1.00 | 0.87 | 0.07 | asserted 1.00 |
| s4 | The proposed method is based on ideas recently exploited by manifold learning approaches, which include hypergraphs, Cartesian products, and connected components. | method 0.80 | 0.11 | 0.02 | asserted 0.99 |
| s5 | The algorithm computes context-sensitive embeddings, which are refined following a rank-based processing flow, while complementary contextual information is incorporated. | method 1.00 | 0.79 | 0.04 | asserted 0.99 |
| s6 | The generated embeddings can be exploited for more effective unsupervised retrieval or semi-supervised classification based on Graph Convolutional Networks. | method 0.81 | 0.46 | 0.09 | hedged possibility 0.59 |
| s7 | Experimental results were conducted on 10 different collections. | method 0.91 | 0.93 | 0.02 | asserted 0.97 |
| s8 | Various features were considered, including the ones obtained with recent Convolutional Neural Networks (CNN) and Vision Transformer (ViT) models. | method 0.97 | 0.78 | 0.02 | asserted 0.92 |
| s9 | High effective results demonstrate the effectiveness of the proposed method on different tasks: unsupervised image retrieval, semi-supervised classification, and person Re-ID. | result 0.89 | 0.94 | 0.50 | asserted 1.00 |
| s10 | The results demonstrate that RFE is competitive or superior to the state-of-the-art in diverse evaluated scenarios. | result 0.70 | 0.92 | 0.79 | asserted 1.00 |
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The paper
Original abstract
Impressive advances in acquisition and sharing technologies have made the growth of multimedia collections and their applications almost unlimited. However, the opposite is true for the availability of labeled data, which is needed for supervised training, since such data is often expensive and time-consuming to obtain. While there is a pressing need for the development of effective retrieval and classification methods, the difficulties faced by supervised approaches highlight the relevance of methods capable of operating with few or no labeled data. In this work, we propose a novel manifold learning algorithm named Rank Flow Embedding (RFE) for unsupervised and semi-supervised scenarios. The proposed method is based on ideas recently exploited by manifold learning approaches, which include hypergraphs, Cartesian products, and connected components. The algorithm computes context-sensitive embeddings, which are refined following a rank-based processing flow, while complementary contextual information is incorporated. The generated embeddings can be exploited for more effective unsupervised retrieval or semi-supervised classification based on Graph Convolutional Networks. Experimental results were conducted on 10 different collections. Various features were considered, including the ones obtained with recent Convolutional Neural Networks (CNN) and Vision Transformer (ViT) models. High effective results demonstrate the effectiveness of the proposed method on different tasks: unsupervised image retrieval, semi-supervised classification, and person Re-ID. The results demonstrate that RFE is competitive or superior to the state-of-the-art in diverse evaluated scenarios.
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