Semantic Anchor Learning with Conflict-Aware Evidential Inference for CLIP-Based Few-Shot Open-Set Recognition

Prompt learning for vision-language models has shown strong performance in few-shot classification and has recently been extended to few-shot open-set recognition through unknown-text modeling. Existing methods construct surrogate unknown semantics using manually selected open words, generated pseudo samples, or statistically derived unknown directions, but they provide limited control over the geometry between known and unknown prompts and typically make open-set decisions from a single response score. We propose Semantic Anchor Learning with Conflict-Aware Evidential Inference (SAEC), a CLIP-based framework that couples unknown semantic construction, boundary regularization, and evidential decision making. SAEC first factorizes auxiliary token embeddings into a sparse dictionary and composes its primitives into synthesized unknown semantic anchors. It then regularizes the shared embedding space with unknown-aware probability regularization, explicit known–unknown separation, and intra-unknown dispersion. At inference time, SAEC converts competing known and unknown similarities into a Dempster–Shafer-inspired mass representation with a residual ignorance term. Across eight standard few-shot open-set benchmarks, SAEC improves AUROC over the directly comparable UTL baseline on all eight settings, with an average gain of 1.35 points and a maximum gain of 3.58 points, while achieving the best closed-set accuracy on six settings among the reported baselines.