Blind to Position, Biased in Language: Probing Mid-Layer Representational Bias in Vision-Language Encoders for Zero-Shot Language-Grounded Spatial Understanding

Vision–Language Encoders (VLEs) are widely adopted as the backbone of zero-shot referring image segmentation (RIS), enabling textguided localization without task-specific training. However, prior works underexplored the underlying biases within mid-layer representations that preserve positional and language-specific information. Through layerwise investigation, we reveal that the conventionally used final-layer multimodal embeddings prioritize global semantic alignment, leading to two coupled consequences. First, vision embeddings exhibit weak sensitivity to positional cues. Second, multilingual text embeddings form language-dependent geometric shifts within the shared space. Motivated by these findings, we identify an underexplored pathway within VLE mid-layers to construct a spatial map, applicable for improving zero-shot RIS by 1–7 mIoU on nine RefCOCO benchmarks. Furthermore, leveraging mixed-language mid-layer embeddings yields enhanced spatial grounding accuracy (+7–8 mIoU and IoU@50), albeit with increased inference cost, and also improves performance on the zero-shot text-to-image retrieval task. Our work opens up the discussion about the effects of effective representational bias probing of VLEs for enhanced spatial grounding. The code is available at https://github.com/kaist-cvml/Biased2Grounded.