ORION: ORthonormal Text Encoding for Universal VLM AdaptatION

Vision-language models (VLMs) have demonstrated remarkable generalization across diverse tasks, yet their performance remains constrained by the quality and geometry of the textual prototypes used to represent classes. Standard zero-shot classifiers, derived from frozen text encoders and handcrafted prompts, may yield correlated or weakly separated embeddings that limit task-specific discriminability. We introduce ORION, a text encoder fine-tuning framework that improves pretrained VLMs using only class names. Our method optimizes, via low-rank adaptation, a novel loss integrating two terms, one promoting pairwise orthogonality between the textual representations of the classes of a given task and the other penalizing deviations from the initial class prototypes. Furthermore, we provide a probabilistic interpretation of our orthogonality penalty, connecting it to the general maximum likelihood estimation (MLE) principle via Huygens' theorem. We report extensive experiments on 11 benchmarks and three large VLM backbones, showing that the refined textual embeddings yield powerful replacements for the standard CLIP prototypes. Added as plug-and-play module on top of various state-of-the-art methods, and across different prediction settings (zero-shot, few-shot and test-time adaptation), ORION improves the performance consistently and significantly. We make the code available at https://github.com/ORION. work in the literature has focused on prompt tuning [41,42] and visual adaptation [28,36], aiming to bridge the domain gap while keeping the base model frozen. Yet, the role of the text encoder, which defines the classifier space of a VLM, still remains greatly unexplored. This overlooked component is central to how the decision boundaries are formed in the shared embedding space and, as we show in this work, its geometry may influence significantly the generalization behavior of VLMs across zero-shot, few-shot and test-time adaptation settings.