Detecting human-object interactions (HOI) in a few-shot setting remains a challenge. Existing meta-learning methods struggle to extract representative features for classification due to the limited data, while existing few-shot HOI models rely on HOI text labels for classification. Moreover, some query images may display visual similarity to those outside their class, such as similar backgrounds between different HOI classes. This makes learning more challenging, especially with limited samples. Bongard-HOI (Jiang et al. 2022) epitomizes this HOI few-shot problem, making it the benchmark we focus on in this paper. In our proposed method, we introduce novel label-uncertain query augmentation techniques to enhance the diversity of the query inputs, aiming to distinguish the positive HOI class from the negative ones. As these augmented inputs may or may not have the same class label as the original inputs, their class label is unknown. Those belonging to a different class become hard samples due to their visual similarity to the original ones. Additionally, we introduce a novel pseudo-label generation technique that enables a mean teacher model to learn from the augmented label-uncertain inputs. We propose to augment the negative support set for the student model to enrich the semantic information, fostering diversity that challenges and enhances the student's learning. Experimental results demonstrate that our method sets a new state-of-the-art (SOTA) performance by achieving 68.74% accuracy on the Bongard-HOI benchmark, a significant improvement over the existing SOTA of 66.59%. In our evaluation on HICO-FS, a more general few-shot recognition dataset, our method achieves 73.27% accuracy, outperforming the previous SOTA of 71.20% in the 5-way 5-shot task.

翻译：在小样本场景下检测人-物交互（HOI）仍是一项挑战。现有的元学习方法因数据有限难以提取具有代表性的分类特征，而现有小样本HOI模型依赖HOI文本标签进行分类。此外，部分查询图像可能与类外样本存在视觉相似性（例如不同HOI类别间的相似背景），这使得学习过程更具难度——尤其是在样本量受限的情况下。Bongard-HOI（Jiang et al. 2022）作为典型的小样本HOI问题基准，是本文的核心研究对象。我们提出一种新颖的标签不确定查询增强技术，通过提升查询输入的多样性来区分正类与负类HOI。由于这些增强后的输入可能保留或改变原始输入的类别标签，其真实类别未知。其中属于异类的样本因与原始样本存在视觉相似性而成为困难样本。我们还提出一种新型伪标签生成技术，使均值教师模型能够从增强后的标签不确定输入中学习。通过扩展学生模型的负类支撑集以丰富语义信息，我们引入多样性挑战增强学生的学习效果。实验结果表明，本方法在Bongard-HOI基准上取得了68.74%的准确率，将现有最优方法（SOTA）的66.59%显著提升至新高度。在更通用的小样本识别数据集HICO-FS评估中，本方法在5-way 5-shot任务上以73.27%的准确率超越先前SOTA的71.20%。