AnyVIA: A Universal Visible-Infrared Adapter for UAV Perception

Vision foundation models (VFMs) provide transferable representations. However, aerial visible-infrared (VI) perception benefits from jointly modeling the complementary cues present in visible and infrared imagery. Most VFMs are pretrained primarily on RGB data and are not explicitly optimized for such cross-spectral modeling. Pretraining a dedicated aerial VI VFM is constrained by the cost of collecting large numbers of cross-spectrally registered image pairs, while existing VI adapters are typically tied to particular encoders and tasks. We introduce AnyVIA, a 299K-parameter encoder-agnostic adapter that transforms and spatially upsamples frozen VFM features for aerial VI perception. AnyVIA is pretrained once and then reused in frozen form across heterogeneous VFMs and downstream tasks. For each modality, a Raw Guidance Encoder (RGE) derives attention queries and keys solely from the input image, while host features enter only as values after parameter-free channel folding. This design decouples AnyVIA's learned weights from the host feature width. During pretraining, shared box prompts anchor both modalities to the same objects, while a directional consistency objective aligns refined feature directions with those of frozen host features extracted from corresponding higher-resolution crops. To provide dense supervision for cross-spectral representation learning, we further construct UAVRGBT235K, a pretraining resource for unmanned aerial vehicle (UAV) VI adaptation. It comprises 117,668 image pairs and 7.15 million automatically generated instance masks, substantially expanding the object-level annotations available for aerial VI pretraining. Across eight held-out UAV benchmarks spanning five perception tasks, the frozen AnyVIA yields improvements for diverse encoders. These results suggest that AnyVIA offers a scalable and reusable alternative to pretraining a dedicated VFM for aerial VI perception.