AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference
Low-light video enhancement (LLVE) remains a challenging task due to severe information degradation under low-illumination conditions. Recent multimodal approaches have significantly improved enhancement performance by incorporating auxiliary modalities, such as event streams and infrared images. However, these methods typically assume the availability of these modalities at inference, which is often not feasible in real-world scenarios. To solve this problem, in this work, we propose AMNet, a unified multimodal framework for LLVE, to support flexible modality-agnostic inference, where auxiliary modalities may be unavailable. To address the issue of modality absence, we introduce a Spatia...
| Authors | Hangfeng Liang, Yutao Hu, Yanhan Hu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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