  Fine-grained image classification is a challenging computer vision task where
various species share similar visual appearances, resulting in
misclassification if merely based on visual clues. Therefore, it is helpful to
leverage additional information, e.g., the locations and dates for data
shooting, which can be easily accessible but rarely exploited. In this paper,
we first demonstrate that existing multimodal methods fuse multiple features
only on a single dimension, which essentially has insufficient help in feature
discrimination. To fully explore the potential of multimodal information, we
propose a dynamic MLP on top of the image representation, which interacts with
multimodal features at a higher and broader dimension. The dynamic MLP is an
efficient structure parameterized by the learned embeddings of variable
locations and dates. It can be regarded as an adaptive nonlinear projection for
generating more discriminative image representations in visual tasks. To our
best knowledge, it is the first attempt to explore the idea of dynamic networks
to exploit multimodal information in fine-grained image classification tasks.
Extensive experiments demonstrate the effectiveness of our method. The t-SNE
algorithm visually indicates that our technique improves the recognizability of
image representations that are visually similar but with different categories.
Furthermore, among published works across multiple fine-grained datasets,
dynamic MLP consistently achieves SOTA results
https://paperswithcode.com/dataset/inaturalist and takes third place in the
iNaturalist challenge at FGVC8
https://www.kaggle.com/c/inaturalist-2021/leaderboard. Code is available at
https://github.com/ylingfeng/DynamicMLP.git
