Posted by Christine Kaeser-Chen, Software Engineer and Serge Belongie, Visiting Faculty, Google AI

In recent years, fine-grained visual recognition competitions (FGVCs), such as the iNaturalist species classification challenge and the iMaterialist product attribute recognition challenge, have spurred progress in the development of image classification models focused on detection of fine-grained visual details in both natural and man-made objects. Whereas traditional image classification competitions focus on distinguishing generic categories (e.g., car vs. butterfly), the FGVCs go beyond entry level categories to focus on subtle differences in object parts and attributes. For example, rather than pursuing methods that can distinguish categories, such as “bird”, we are interested in identifying subcategories such as “indigo bunting” or “lazuli bunting.”

Previous challenges attracted a large number of talented participants who developed innovative new models for image recognition, with more than 500 teams competing at FGVC5 at CVPR 2018. FGVC challenges have also inspired new methods such as domain-specific transfer learning and estimating test-time priors, which have helped fine-grained recognition tasks reach state-of-the-art performance on several benchmarking datasets.

In order to further spur progress in FGVC research, we are proud to sponsor and co-organize the 6th annual workshop on Fine-Grained Visual Categorization (FGVC6), to be held on June 17th in Long Beach, CA at CVPR 2019. This workshop brings together experts in computer vision with specialists focusing on biodiversity, botany, fashion, and the arts, to address the challenges of applying fine-grained visual categorization to real-life settings.

This Year’s Challenges
This year there will be a wide variety of competition topics, each highlighting unique challenges of fine-grained visual categorization, including an updated iNaturalist challenge, fashion & products, wildlife camera traps, food, butterflies & moths, fashion design, and cassava leaf disease. We are also delighted to introduce two new partnerships with world class institutions—The Metropolitan Museum of Art for the iMet Collection challenge and the New York Botanical Garden for the Herbarium challenge.

The FGVC workshop at CVPR focuses on subordinate categories, including (from left to right, top to bottom) animal species from wildlife camera traps, retail products, fashion attributes, cassava leaf disease, Melastomataceae species from herbarium sheets, animal species from citizen science photos, butterfly and moth species, cuisine of dishes, and fine-grained attributes for museum art objects.

In the iMet Collection challenge, participants compete to train models on artistic attributes including object presence, culture, content, theme, and geographic origin. The Metropolitan Museum of Art provided a large training dataset for this task based on subject matter experts’ descriptions of their museum collections. This dataset highlights the challenge of inferring fine-grained attributes that are grounded in the visual context indirectly (e.g., period, culture, medium).

A diverse sample of images included in the iMet Collection challenge dataset. Images were taken from the Metropolitan Museum of Art’s public domain dataset.

The iMet Collection challenge is also noteworthy for its status as the first image-based Kernels-only competition, a recently introduced option on Kaggle that levels the playing field for data scientists who might not otherwise have access to adequate computational resources. Kernel competitions provide all participants with the same hardware allowances, giving rise to a more balanced competition. Moreover, the winning models tend to be simpler than their counterparts in other competitions, since the participants must work within the compute constraints imposed by the Kernels platform. At the time of writing, the iMet Collection challenge has over 250 participating teams.

In the Herbarium challenge, researchers are invited to tackle the problem of classifying species from the flowering plant family Melastomataceae. This challenge is distinguished from the iNaturalist competition, since the included images depict dried specimens preserved on herbarium sheets, exclusively. Herbarium sheets are essential to plant science, as they not only preserve the key details of the plants for identification and DNA analysis, but also provide a rare perspective into plant ecology in a historical context. As the world’s second largest herbarium, NYBG’s Steere Herbarium collection contributed a dataset of over 46,000 specimens for this year’s challenge.

In the Herbarium challenge, participants will identify species from the flowering plant family Melastomataceae. The New York Botanical Garden (NYBG) provided a dataset of over 46,000 herbarium specimens including over 680 species. Images used with permission of the NYBG.

Every one of this year’s challenges requires deep engagement with subject matter experts, in addition to institutional coordination. By teeing up image recognition challenges in a standard format, the FGVC workshop paves the way for technology transfer from the top of the Kaggle leaderboards into the hands of everyday users via mobile apps such as Seek by iNaturalist and Merlin Bird ID. We anticipate the techniques developed by our competition participants will not only push the frontier of fine-grained recognition, but also be beneficial for applying machine vision to advance scientific exploration and curatorial studies.

Invitation to Participate
We invite teams to participate in these competitions to help advance the state-of-the-art in fine-grained image recognition. Deadlines for entry into the competitions range from May 26 to June 3, depending on the challenge. The results of these competitions will be presented at the FGVC6 workshop at CVPR 2019, and will provide broad exposure to the top performing teams. We are excited to encourage the community's development of more accurate and broadly impactful algorithms in the field of fine-grained visual categorization!

Acknowledgements
We’d like to thank our colleagues and friends on the FGVC6 organizing committee for working together to advance this important area. At Google we would like to thank Hartwig Adam, Chenyang Zhang, Yulong Liu, Kiat Chuan Tan, Mikhail Sirotenko, Denis Brulé, Cédric Deltheil, Timnit Gebru, Ernest Mwebaze, Weijun Wang, Grace Chu, Jack Sim, Andrew Howard, R.V. Guha, Srikanth Belwadi, Tanya Birch, Katherine Chou, Maggie Demkin, Elizabeth Park, and Will Cukierski.

Posted by Yang Song, Staff Software Engineer and Serge Belongie, Visiting Faculty, Google Research

Thanks to recent advances in deep learning, the visual recognition abilities of machines have improved dramatically, permitting the practical application of computer vision to tasks ranging from pedestrian detection for self-driving cars to expression recognition in virtual reality. One area that remains challenging for computers, however, is fine-grained and instance-level recognition. Earlier this month, we posted an instance-level landmark recognition challenge for identifying individual landmarks. Here we focus on fine-grained visual recognition, which is to distinguish species of animals and plants, car and motorcycle models, architectural styles, etc. For computers, discriminating fine-grained categories is challenging because many categories have relatively few training examples (i.e., the long tail problem), the examples that do exist often lack authoritative training labels, and there is variability in illumination, viewing angle and object occlusion.

To help confront these hurdles, we are excited to announce the 2018 iNaturalist Challenge (iNat-2018), a species classification competition offered in partnership with iNaturalist and Visipedia (short for Visual Encyclopedia), a project for which Caltech and Cornell Tech received a Google Focused Research Award. This is a flagship challenge for the 5th International Workshop on Fine Grained Visual Categorization (FGVC5) at CVPR 2018. Building upon the first iNaturalist challenge, iNat-2017, iNat-2018 spans over 8000 categories of plants, animals, and fungi, with a total of more than 450,000 training images. We invite participants to enter the competition on Kaggle, with final submissions due in early June. Training data, annotations, and links to pretrained models can be found on our GitHub repo.

iNaturalist has emerged as a world leader for citizen scientists to share observations of species and connect with nature since its founding in 2008. It hosts research-grade photos and annotations submitted by a thriving, engaged community of users. Consider the following photo from iNaturalist:

The map on the right shows where the photo was taken. Image credit: Serge Belongie.

You may notice that the photo on the left contains a turtle. But did you also know this is a Trachemys scripta, common name “Pond Slider?” If you knew the latter, you possess knowledge of fine-grained or subordinate categories.

In contrast to other image classification datasets such as ImageNet, the dataset in the iNaturalist challenge exhibits a long-tailed distribution, with many species having relatively few images. It is important to enable machine learning models to handle categories in the long-tail, as the natural world is heavily imbalanced – some species are more abundant and easier to photograph than others. The iNaturalist challenge will encourage progress because the training distribution of iNat-2018 has an even longer tail than iNat-2017.

Distribution of training images per species for iNat-2017 and iNat-2018, plotted on a log-log scale, illustrating the long-tail behavior typical of fine-grained classification problems. Image Credit: Grant Van Horn and Oisin Mac Aodha.

Along with iNat-2018, FGVC5 will also host the iMaterialist 2018 challenge (including a furniture categorization challenge and a fashion attributes challenge for product images) and a set of “FGVCx” challenges representing smaller scale – but still significant – challenges, featuring content such as food and modern art.

FGVC5 will be showcased on the main stage at CVPR 2018, thereby ensuring broad exposure for the top performing teams. This project will advance the state-of-the-art in automatic image classification for real world, fine-grained categories, with heavy class imbalances, and large numbers of classes. We cordially invite you to participate in these competitions and help move the field forward!

Acknowledgements
We’d like to thank our colleagues and friends at iNaturalist, Visipedia, and FGVC5 for working together to advance this important area. At Google we would like to thank Hartwig Adam, Weijun Wang, Nathan Frey, Andrew Howard, Alessandro Fin, Yuning Chai, Xiao Zhang, Jack Sim, Yuan Li, Grant Van Horn, Yin Cui, Chen Sun, Yanan Qian, Grace Vesom, Tanya Birch, Wendy Kan, and Maggie Demkin.