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Context-Aware Interpretable Representations for Retrieval and Graph Convolutional Network Classification
Thiago César Castilho Almeida, Gustavo Rosseto Letício, Vinicius Atsushi Sato Kawai, Daniel Carlos Guimarães Pedronette
TL;DR
The paper addresses geometric and interpretability gaps in visual representations, where pairwise similarity misses manifold structure and dense dimensions lack semantic meaning. It introduces an unsupervised modular framework that refines rankings with manifold learning, selects prototypes, and generates sparse rank-based embeddings. Across retrieval and GCN classification benchmarks, the representations reduce dimensionality while preserving or improving effectiveness and providing human-centric explanations.
Problem
Pairwise similarity measures can miss intrinsic manifold geometry, while dense representation dimensions lack explicit semantic meaning for human understanding.
Method
An unsupervised modular framework performs manifold refinement, prototype discovery, and interpretable embedding generation using rank-based graph representations.
Results
The approach preserves or improves effectiveness in image retrieval and semi-supervised GCN classification while producing sparse, self-explaining representations.
Takeaways & Limitations
Prototype-defined dimensions provide intrinsic interpretability and qualitative human-centric explanations alongside dimensionality reduction.
Takeaways & Limitations
Adding new samples requires recomputing ranked lists, while neighborhood parameters require dataset-specific tuning and some backbone–manifold combinations degrade performance.
Abstract
from arXiv · showhide
The advances in visual information modeling and representation during the last decades are remarkable, mainly supported by Convolutional Neural Networks, Transformer-based, and Foundation Models. Despite this progress, critical challenges regarding the nature of similarity assessment and model transparency have been neglected. A primary concern is the Geometric Gap, where traditional pairwise measures fail to capture the intrinsic geometry of the dataset manifold. Furthermore, the Interpretability Gap persists, as representations often lack alignment with human cognition. Therefore, how to provide interpretability to representations while maintaining low dimensionality and high effectiveness in downstream tasks remains an open challenge. In this paper, we propose a novel unsupervised framework that integrates Manifold Learning strategies with Rank-based Interpretable Graph Embeddings. Our approach effectively bridges these gaps by first characterizing the contextual information of the dataset through manifold analysis and subsequently generating sparse, self-explainable embeddings. The proposed approach employs a flexible formulation, allowing different Manifold Learning and Representation Learning strategies. Extensive experimental evaluation across diverse datasets and features demonstrates that our Context-Aware representations not only provide intrinsic interpretability and dimensionality reduction but also maintain or enhance effectiveness in downstream tasks, specifically in image retrieval and semi-supervised classification using Graph Convolutional Networks (GCNs).
1 Introduction
The paper identifies a dual challenge: pairwise similarity measures overlook manifold geometry, while dense representations lack human-aligned semantic meaning. It proposes context-aware rank-based representations to improve geometry, interpretability, dimensionality, and downstream effectiveness.
- Euclidean-style pairwise measures can miss intrinsic data curvature, creating the Geometric Gap.
- Dense representation dimensions lack explicit semantic meaning, creating an Interpretability Gap that limits trust and human understanding.
- Existing interpretability and graph-based approaches address the gaps separately but retain restrictive assumptions, supervision costs, or unreliable neighborhoods.
- The proposed framework combines manifold learning with rank-based embeddings to correct geometry before selecting interpretable prototypes.
- Across diverse datasets and features, the approach maintains or surpasses high-dimensional effectiveness while reducing dimensionality and exposing semantic dimensions.
2 Related Work
Related work spans graph representation learning, disentanglement, and interpretable-by-design models. The paper positions rank-based prototype representations as a geometrically faithful alternative to Euclidean prototype selection and strict factor independence.
- Network Representation Learning: Network Representation Learning embeds graph nodes while preserving topology, evolving from shallow random-walk methods toward GNNs that combine attributes and structure.
- Interpretable Graph Embedding: Interpretable graph embeddings address opaque GNN dimensions by making each dimension encode similarity to a specific prototype node.
- Disentangled Representation Learning: Disentangled Representation Learning separates explanatory factors, but strict independence can be theoretically unsupported and distort correlated multimedia concepts.
- Rank-based Interpretability: Rank-based methods emphasize complementary prototype coverage rather than statistical orthogonality, preserving natural correlations and manifold geometry.
- Interpretable-by-Design Models: Prototype-based and language-based interpretable models can require specialized training, pretrained-backbone adaptation, or assumptions that visual features are verbally describable.
- Motivation: Training-free prototype methods motivate the proposed framework because Euclidean assumptions can select unrepresentative prototypes in non-Euclidean latent spaces.
3 Proposed Approach
The framework refines contextual rankings, constructs a rank-weighted graph, selects diverse prototypes, and projects each object into a low-dimensional similarity vector. This sequence targets both topological fidelity and intrinsic interpretability.
- The unsupervised modular pipeline has three stages: Manifold Refinement, Prototype Discovery, and Interpretable Embedding Generation.
- Problem Formulation: The learned transformation maps high-dimensional features into d≪D embeddings that should preserve manifold topology and define dimensions through prototypes.
- Stage 1: Manifold Refinement: Manifold refinement replaces noisy rankings by exploiting local structure through UMAP, RDPAC, LHRR, or BFSTREE.
- Stage 2: Graph Construction: The refined rankings support graph construction with rank-based edge weights, including RaDE logarithmic decay and GRaCE rank-correlation measures.
- Stage 2: Prototype Selection: Greedy prototype selection maximizes candidate effectiveness while penalizing similarity to already selected prototypes, encouraging diverse manifold coverage.
- Stage 3: Interpretable Embedding Generation: Each embedding dimension records rank-based similarity to its corresponding prototype, making the vector semantically transparent while using refined rankings.
4 Experimental Setup
The evaluation covers four datasets spanning general and fine-grained object domains, using three ImageNet-1K-pretrained CNN or Transformer backbones. The study examines retrieval and semi-supervised classification effectiveness.
- Datasets: The benchmarks comprise Oxford17Flowers, Corel5k, Oxford-IIIT Pet, and CUB-200, covering 17 to 200 classes and 1,360 to 11,788 images.
- Features: Feature extraction uses ViT-B/16 with d=768, Swin-Tf with d=1024, and ConvNeXt-Base with d=1024.
4.2 Experimental Protocol
The framework is evaluated for content-based image retrieval and semi-supervised GCN classification, using ranking and label-scarce classification protocols.
- Evaluation covers Content-Based Image Retrieval and semi-supervised classification using Graph Convolutional Networks.
- Retrieval treats every image as a query and the remaining images as the gallery.
- Retrieval effectiveness is measured with Mean Average Precision at depth 1000 (MAP@1000), which penalizes relevant items ranked low.
- The retrieval depth of 1000 follows prior work to ensure comparability with the same evaluation protocol.
- GCN evaluation uses five rounds of 10-fold stratified cross-validation, with 10% of each fold for training and 90% for testing.
4.3 Implementation Details and Parameters
Implementation evaluates UMAP alongside three Context-Aware Similarity methods and configures rank-based graph construction and prototype selection with dataset-dependent neighborhoods.
- The manifold-learning stage compares neighbor embedding with Context-Aware Similarity methods.
- The Context-Aware Similarity methods are RDPAC, LHRR, and BFSTREE from the Unsupervised Distance Learning Framework.
- UMAP uses its standard implementation with default parameters, while LHRR and BFSTREE use initial ranked lists of L=1000.
- Rank-based graph construction uses RaDE and GRaCE with neighborhood parameters matched to the manifold-learning stage, using k values from 60 to 200 depending on the dataset.
- Prototype selection uses Reciprocal Density for node-effectiveness estimation and JaccardMax for rank correlation.
Rank-based Graph Embedding.
The implementation uses dataset-dependent embedding dimensions and constructs the classification input graph with k-NN or Reciprocal k-NN connectivity.
- Rank-based Graph Embedding.: Embedding dimension is d=128 for Flowers, Corel5k, and Pets, and d=256 for CUB-200.
- Rank-based Graph Embedding.: The CUB-200 dimension increases to d=256 to accommodate the benchmark’s 200 classes and higher semantic diversity.
- GCN Configuration.: The semi-supervised classification graph uses either k-Nearest Neighbor or Reciprocal k-NN topology with k=40.
- GCN Configuration.: The Simple Graph Convolution Network is optimized with Adam for 200 epochs.
- GCN Configuration.: The learning rate is 10^-3 for Flowers, Corel5k, and Pets, and 10^-2 for CUB-200.
GCN Configuration.
The experiments use dedicated software packages for rank-based embeddings, UMAP, Context-Aware Similarity Learning, and GCN training.
- GCN Configuration.: Rank-based Graph Embedding methods use the interpretable-embeddings package.
- GCN Configuration.: UMAP uses the umap-learn library, while Context-Aware Similarity Learning uses the pyUDLF wrapper.
- GCN Configuration.: PyTorch is used to train the GCN models.
5 Results and Discussion
Across retrieval and semi-supervised classification, context-aware manifold learning combined with interpretable embeddings generally preserves or improves effectiveness while reducing dimensionality and exposing prototype-based semantics. Results also show method and backbone sensitivity, with some combinations degrading performance and the framework requiring dataset-specific tuning.
- Image retrieval: GRaCE combined with rank-based manifold learning achieved the best Flowers retrieval performance, surpassing original and isolated interpretable features.
- Image retrieval: 96.90% MAP was achieved by GRaCE+BFSTREE on Corel5k with Swin-Tf, compared with 73.27% for the original Swin-Tf feature.
- Image retrieval: Except for Swin-Tf on Pets, GRaCE with manifold learning generally achieved the best results across Corel5k, Pets, and CUB-200 features.
- Comparison with state-of-the-art: The proposed framework achieved the highest MAP across all datasets with ViT-B/16 features, unlike DeepWalk's opaque dimensions and DINE's effectiveness loss.
- Semi-supervised classification: GCN classification accuracy was robustly maintained in most scenarios, with RaDE combined with LHRR or UMAP often achieving the best results.The representations compressed features, for example from 1024 to 128 dimensions, while retaining semantic interpretability and discriminative power.
- Qualitative and visual analysis: Qualitative analysis compared original features, interpretable methods, and GRaCE+RDPAC retrieval, while prototype heatmaps linked dimensions to instance–prototype similarity.
- Limitations: Some backbone–manifold combinations degraded performance, while new samples require ranked-list recomputation and neighborhood parameters k and L require dataset-specific tuning.
6 Conclusions
The paper presents context-aware interpretable representations that combine rank-based manifold learning with prototype-driven embedding generation. Across benchmarks, the approach preserves or improves retrieval and semi-supervised GCN classification effectiveness, while qualitative analysis supports intuitive explanations.
- The framework combines rank-based manifold learning with prototype-driven embedding generation to produce context-aware interpretable representations.
- The resulting structures are sparse, self-explaining, and faithful to intrinsic data topology.
- Across diverse benchmarks, the approach preserves or improves effectiveness in image retrieval and semi-supervised GCN classification.
- Future work will investigate limitations and integrate self-supervised contrastive learning while maintaining structural transparency.