Open-Vocabulary Segmentation in the Dark

Open-vocabulary segmentation has achieved substantial progress in recent years, yet existing methods are mainly developed for well-illuminated images and suffer significant performance degradation under low-light conditions. In this work, we propose OWL-Net, a RAW-assisted framework for open-vocabulary segmentation in the dark. OWL-Net exploits the fine-grained intensity and structural information preserved in RAW data during training, while requiring only RGB input during inference. Specifically, we introduce a RAW EnCoding (REC) module that represents large-range RAW values as the binary bit sequence and extracts RAW features directly at the bit level, enabling critical details within RAW data to be better preserved during feature extraction. Meanwhile, to avoid the reliance on RAW input during inference, we further develop an RGB Representation Enhancement (RRE) module to progressively recover the overwhelmed details and lift the degraded RGB features to RAW-aware space. In addition, we construct LOVS, a real-world Low-light Open-Vocabulary Segmentation dataset containing 1,000 aligned normal-/low-light image pairs, covering 220 semantic categories with 6,949 object-level masks. Extensive experiments on both synthetic low-light benchmarks and LOVS demonstrate that OWL-Net consistently improves different open-vocabulary segmentation frameworks under low-light conditions, without requiring RAW input or explicit image enhancement during inference. Our code and dataset will be released upon acceptance.