  In real-world scenarios, using multiple modalities like visible (RGB) and
infrared (IR) can greatly improve the performance of a predictive task such as
object detection (OD). Multimodal learning is a common way to leverage these
modalities, where multiple modality-specific encoders and a fusion module are
used to improve performance. In this paper, we tackle a different way to employ
RGB and IR modalities, where only one modality or the other is observed by a
single shared vision encoder. This realistic setting requires a lower memory
footprint and is more suitable for applications such as autonomous driving and
surveillance, which commonly rely on RGB and IR data. However, when learning a
single encoder on multiple modalities, one modality can dominate the other,
producing uneven recognition results. This work investigates how to efficiently
leverage RGB and IR modalities to train a common transformer-based OD vision
encoder, while countering the effects of modality imbalance. For this, we
introduce a novel training technique to Mix Patches (MiPa) from the two
modalities, in conjunction with a patch-wise modality agnostic module, for
learning a common representation of both modalities. Our experiments show that
MiPa can learn a representation to reach competitive results on traditional
RGB/IR benchmarks while only requiring a single modality during inference. Our
code is available at: https://github.com/heitorrapela/MiPa.
