Whereas visual recognition research mainly focused on two very different situations; distinguishing between basic-level categories (category recognition) or recognizing specific instances (instance recognition), developing algorithms for automatically discriminating categories with only small subtle visual differences (fine-grained recognition) is a new challenge that just started in the last couple of years. My research in fine-grained recognition aims at developing new learning algorithms for fine-grained and part-based recognition.


Generalized orderless pooling performs implicit salient matching.
Marcel Simon and Yang Gao and Trevor Darrell and Joachim Denzler and Erik Rodner.
International Conference on Computer Vision (ICCV). 2017. (accepted for publication) BibTeX pdf


Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches.
Erik Rodner and Marcel Simon and Bob Fisher and Joachim Denzler.
British Machine Vision Conference (BMVC). 2016. BibTeX pdf supplementary
SeaCLEF 2016: Object Proposal Classification for Fish Detection in Underwater Videos.
Jonas Jäger and Erik Rodner and Joachim Denzler and Viviane Wolff and Klaus Fricke-Neuderth.
Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum. 481-489. 2016. BibTeX pdf


Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks.
Marcel Simon and Erik Rodner.
International Conference on Computer Vision (ICCV). 1143-1151. 2015. BibTeX pdf www more ...

Abstract: Part models of object categories are essential for challenging recognition tasks, where differences in categories are subtle and only reflected in appearances of small parts of the object. We present an approach that is able to learn part models in a completely unsupervised manner, without part annotations and even without given bounding boxes during learning. The key idea is to find constellations of neural activation patterns computed using convolutional neural networks. In our experiments, we outperform existing approaches for fine-grained recognition on the CUB200-2011, Oxford PETS, and Oxford Flowers dataset in case no part or bounding box annotations are available and achieve state-of-the-art performance for the Stanford Dog dataset. We also show the benefits of neural constellation models as a data augmentation technique for fine-tuning. Furthermore, our paper unites the areas of generic and fine-grained classification, since our approach is suitable for both scenarios.
Fine-grained Classification of Identity Document Types with Only One Example.
Marcel Simon and Erik Rodner and Joachim Denzler.
Machine Vision Applications (MVA). 126 - 129. 2015. BibTeX pdf www more ...

Abstract: This paper shows how to recognize types of identity documents, such as passports, using state-of-the-art visual recognition approaches. Whereas recognizing individual parts on identity documents with a standardized layout is one of the old classics in computer vision, recognizing the type of the document and therefore also the layout is a challenging problem due to the large variation of the documents. In our paper, we evaluate different techniques for this application including feature representations based on recent achievements with convolutional neural networks.
Fine-grained Recognition Datasets for Biodiversity Analysis.
Erik Rodner and Marcel Simon and Gunnar Brehm and Stephanie Pietsch and J. Wolfgang Wägele and Joachim Denzler.
CVPR Workshop on Fine-grained Visual Classification (CVPR-WS). 2015. BibTeX pdf www


Exemplar-specific Patch Features for Fine-grained Recognition.
Alexander Freytag and Erik Rodner and Trevor Darrell and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). 144-156. 2014. BibTeX pdf code supplementary more ...

Abstract: In this paper, we present a new approach for fine-grained recognition or subordinate categorization, tasks where an algorithm needs to reliably differentiate between visually similar categories, e.g. different bird species. While previous approaches aim at learning a single generic representation and models with increasing complexity, we propose an orthogonal approach that learns patch representations specifically tailored to every single test exemplar. Since we query a constant number of images similar to a given test image, we obtain very compact features and avoid large-scale training with all classes and examples. Our learned mid-level features are build on shape and color detectors estimated from discovered patches reflecting small highly discriminative structures in the queried images. We evaluate our approach for fine-grained recognition on the CUB-2011 birds dataset and show that high recognition rates can be obtained by model combination.
Part Detector Discovery in Deep Convolutional Neural Networks.
Marcel Simon and Erik Rodner and Joachim Denzler.
Asian Conference on Computer Vision (ACCV). 162-177. 2014. BibTeX pdf code more ...

Abstract: Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called part detector discovery (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training.
Part Localization by Exploiting Deep Convolutional Networks.
Marcel Simon and Erik Rodner and Joachim Denzler.
ECCV Workshop on Parts and Attributes (ECCV-WS). 2014. BibTeX pdf www
Birds of a Feather Flock Together - Local Learning of Mid-level Representations for Fine-grained Recognition.
Alexander Freytag and Erik Rodner and Joachim Denzler.
ECCV Workshop on Parts and Attributes (ECCV-WS). 2014. BibTeX pdf www code presentation
Nonparametric Part Transfer for Fine-grained Recognition.
Christoph Göring and Erik Rodner and Alexander Freytag and Joachim Denzler.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2489-2496. 2014. BibTeX pdf www code presentation more ...

Abstract: In the following paper, we present an approach for fine-grained recognition based on a new part detection method. In particular, we propose a nonparametric label transfer technique which transfers part constellations from objects with similar global shapes. The possibility for transferring part annotations to unseen images allows for coping with a high degree of pose and view variations in scenarios where traditional detection models (such as deformable part models) fail. Our approach is especially valuable for fine-grained recognition scenarios where intraclass variations are extremely high, and precisely localized features need to be extracted. Furthermore, we show the importance of carefully designed visual extraction strategies, such as combination of complementary feature types and iterative image segmentation, and the resulting impact on the recognition performance. In experiments, our simple yet powerful approach achieves 35.9% and 57.8% accuracy on the CUB-2010 and 2011 bird datasets, which is the current best performance for these benchmarks.


Fine-grained Categorization - Short Summary of our Entry for the ImageNet Challenge 2012.
Christoph Göring and Alexander Freytag and Erik Rodner and Joachim Denzler.
arXiv preprint arXiv:1310.4759. 2013. BibTeX pdf www