Latent Enhancing AutoEncoder for Occluded Image Classification
| Type of publication: | Conference paper |
| Citation: | Kotwal_ICIP_2024 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of International Conference on Image Processing |
| Year: | 2024 |
| Month: | October |
| Abstract: | Large occlusions result in a significant decline in image classification accuracy. During inference, diverse types of unseen occlusions introduce out-of-distribution data to the classification model, leading to accuracy dropping as low as 50%. As occlusions encompass spatially connected regions, conventional methods involving feature reconstruction are inadequate for enhancing classification performance. We introduce LEARN: Latent Enhancing feAture Reconstruction Network– An autoencoder based network that can be incorporated into the classification model before its classifier head without modifying the weights of classification model. In addition to reconstruction and classification losses, training of LEARN effectively combines intra- and inter-class losses calculated over its latent space—which lead to improvement in recovering latent space of occluded data, while preserving its class-specific discriminative information. On the OccludedPASCAL3D+ dataset, the proposed LEARN outperforms standard classification models (VGG16 and ResNet-50) by a large margin and up to 2% over state-of-the-art methods. In cross-dataset testing, our method improves the average classification accuracy by more than 5% over the state-of-the-art methods. In every experiment, our model consistently maintains excellent accuracy on in-distribution data. |
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| Projects |
Biometrics Center |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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