This Year’s Challenges
FGVC7 will feature six challenges, four of which represent sequels to past offerings, and two of which are brand new.
IniWildCam, the challenge is to identify different species of animals in camera trap images. Like its predecessors in 2018 and 2019, this year’s competition makes use of data from static, motion-triggered cameras used by biologists to study animals in the wild. Participants compete to build models that address diverse regions from around the globe, with a focus on generalization to held-out camera deployments within those regions, which exhibit differences in device model, image quality, local environment, lighting conditions, and species distributions, making generalization difficult.
It has been shown that species classification performance can be dramatically improved byusing informationbeyondthe image itself. In addition, since an ecosystem can be monitored in a variety of ways (e.g., camera traps, citizen scientists, remote sensing), each of which has its own strengths and limitations, it is important to facilitate the exploration of techniques for combining these complementary modalities. To this end, the competition provides a time series of remote sensing imagery for each camera trap location, as well as images from theiNaturalist competition datasetsfor species in the camera trap data.
TheHerbarium Challenge, now in its second year, entails plant species identification, based on a large, long-tailed collection of herbarium specimens. Developed in collaboration with theNew York Botanical Garden(NYBG), this challenge features over 1 million images representing over 32,000 plant species. Last year’s challenge was based on 46,000 specimens for 680 species. Being able to recognize species from historical herbarium collections can not only help botanists better understand changes in plant life on our planet, but also offers a unique opportunity to identify previously undescribed new species in the collection.
In this year’siMat Fashionchallenge, participants compete to perform apparelinstance segmentationand fine-grained attribute classification. The goal of this competition is to push the state of the art in fine-grained segmentation by joining forces between the fashion and computer vision communities. This challenge is in its third iteration, growing both in size and level of detail over past years’ offerings.
The last of the sequels isiMet, in which participants are challenged with building algorithms for fine-grained attribute classification on works of art. Developed in collaboration with theMetropolitan Museum of Art, the dataset has grown significantly since the 2019 edition, with a wide array of new cataloguing information generated by subject matter experts including multiple object classifications, artist, title, period, date, medium, culture, size, provenance, geographic location, and other related museum objects within the Met’s collection.
Semi-Supervised Avesis one of the new challenges at this year’s workshop. While avian data fromiNaturalisthas featured prominently in past FGVC challenges, this challenge focuses on the problem of learning from partially labeled data, a form ofsemi-supervised learning. The dataset is designed to expose some of the challenges encountered in realistic settings, such as the fine-grained similarity between classes, significant class imbalance, and domain mismatch between the labeled and unlabeled data.
Rounding out the set of challenges isPlant Pathology. In this challenge, the participants attempt to spot foliar diseases of apples using a reference dataset of expert-annotated diseased specimens. While this particular challenge is new to the FGVC community, it is the second such challenge to involve plant disease, the first beingiCassavaat last year’s FGVC.