AnchorScore: A CLIP-Based Diagnostic of MLLM Annotation Difficulty
Multimodal large language models (MLLMs) are widely used for automated annotation, yet their per-class accuracy varies widely (e.g., 12%-98% across the 13 classes of three classroom sub-datasets) and is expensive to measure: evaluating one 27B MLLM on 5,416 validation images takes roughly 14 hours, whereas a frozen-CLIP pass over the same images completes in about 3 minutes. A low-cost signal for ranking classes by expected MLLM annotation difficulty a priori remains underexplored. Building on the AnchorProxy construct (per-class zero-shot CLIP accuracy) introduced in the companion study, this paper systematically evaluates its full-frame formulation, termed AnchorScore here, as an a prio...
| Authors | Yan Ma, Lizhuo Zhang |
|---|---|
| Primary category | cs.CV |
| Published | 17 August 2026 |
| Last updated at source | 5 September 2026 |
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