只学正向提示+直接学习负向嵌入，比传统双提示更有效
Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations
- 仅学习正向提示，负向用特征空间直接优化
- 在标签缺失率低时，性能接近双提示方法，但参数少16倍
- 证明负向提示可能因训练数据缺失而降低效果，适合资源受限场景
视觉-语言模型（如CLIP）通过提示学习适应部分标注的多标签识别任务，即为每个类别学习正负提示，将其嵌入对齐到共享视觉-文本特征空间。我们提出PositiveCoOp和NegativeCoOp，分别仅学习正提示或负提示，另一类使用不依赖文本编码器的直接学习嵌入。实证分析显示，负提示会损害多标签识别性能；仅学习正提示并结合学习的负嵌入（PositiveCoOp）优于双提示方法。此外，当缺失标签比例较低时，仅使用视觉特征的基线模型性能已接近双提示方法，且训练计算量减半、参数减少16倍。
原文摘要 · Abstract (English)
Vision-language models (VLMs) like CLIP have been adapted for Multi-Label Recognition (MLR) with partial annotations by leveraging prompt-learning, where positive and negative prompts are learned for each class to associate their embeddings with class presence or absence in the shared vision-text feature space. While this approach improves MLR performance by relying on VLM priors, we hypothesize that learning negative prompts may be suboptimal, as the datasets used to train VLMs lack image-caption pairs explicitly focusing on class absence. To analyze the impact of positive and negative prompt learning on MLR, we introduce PositiveCoOp and NegativeCoOp, where only one prompt is learned with VLM guidance while the other is replaced by an embedding vector learned directly in the shared feature space without relying on the text encoder. Through empirical analysis, we observe that negative prompts degrade MLR performance, and learning only positive prompts, combined with learned negative embeddings (PositiveCoOp), outperforms dual prompt learning approaches. Moreover, we quantify the performance benefits that prompt-learning offers over a simple vision-features-only baseline, observing that the baseline displays strong performance comparable to dual prompt learning approach (DualCoOp), when the proportion of missing labels is low, while requiring half the training compute and 16 times fewer parameters
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