Papers › RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
Leonardo Scabini, Kallil M. Zielinski, Lucas C. Ribas, Wesley N. Gonçalves, Bernard De Baets, Odemir M. Bruno
Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature aggregation modules around a pre-trained backbone and then fine-tuning the new architecture on specific texture recognition tasks. Here we propose a new method named \textbf{R}andom encoding of \textbf{A}ggregated \textbf{D}eep \textbf{A}ctivation \textbf{M}aps (RADAM) which extracts rich texture representations without ever changing the backbone. The technique consists of encoding the output at different depths of a pre-trained deep convolutional network using a Randomized Autoencoder (RAE). The RAE is trained locally to each image using a closed-form solution, and its decoder weights are used to compose a 1-dimensional texture representation that is fed into a linear SVM. This means that no fine-tuning or backpropagation is needed. We explore RADAM on several texture benchmarks and achieve state-of-the-art results with different computational budgets. Our results suggest that pre-trained backbones may not require additional fine-tuning for texture recognition if their learned representations are better encoded.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | DTD | RADAM (ConvNeXt-L) | Accuracy | 84.0 | #2 of 11 | Archive leaderboard | report |
| Image Classification | FMD (materials) | RADAM (ConvNeXt-L) | Accuracy (%) | 95.2 | #1 of 1 | Archive leaderboard | report |
| Image Classification | KTH-TIPS2 | RADAM (ConvNeXt-XL) | Accuracy (%) | 94.4 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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