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Nature and wildlife observation is the practice of notign both the occurrence and abundance of plant or animal species at a specific location and time. Common exam-ples of this type of activity are bird watching (birding), insect collecting, and plant observation (botanizing), and these are widely accepted as both recreational and scien-tific activities in their respective fields. However, many highly-similar species are difficult to disambiguate; identi-fying an observed specimen requires expert knowledge and experience in many cases. This hard problem is called Fine-grained Visual Categorization (FGVC) and focuses on dif-ferentiating between hard-to-distinguish object classes. Ex-amples of such fine-level classification include discriminatign between similar species of plants and animals or iden-tifying the make and model of vehicles, instead of recognizing these objects at a coarse level. An FGVC example of butterflies is shown in Figure 1. These two species have similar colors and shapes, but the patterns on the wings are distinct. When presented with near-identical poses as in the figure, this classification can be performed very effectively by a machine. However, in more extreme conditions of pose, illumination, occlusion, etc, the task becomes much harder. While machines struggle in such scenarios, humans can still find the needed visual cues and differences by fac-toring in the pose of the butterfly and comparing patterns on common parts; in part, because humans can infer an ob-ject's rough 3D shape, understand the lighting and camera angle, and even envision what it would look like from an-other pose. Humans have developed a 3D understanding of a butterfly because we have seen moving butterflies previ-ously. What if machines had the same information about the object? Information such as object pose, camera angle, object texture, and part labels, would undoubtedly help im-prove performance on the FGVC task.