Hierarchical Reasoning Network with Contrastive Learning for Few-Shot Human-Object Interaction Recognition

Few-shot learning (FSL) for human-object interaction aims at classifying samples of new unseen HOI classes with only a few labeled samples available. Although progress has been made in few-shot human-object interaction, most of the existing methods encounter two issues in handling fine-grained interactions: the inability to capture more subtle interactive clues and the inadequacy in learning from data with low inter-class variance. To tackle the first issue, we propose a hierarchical reasoning network to integrate multi-level interactive clues (from coarse to fine-grained) for strengthening HOI representations. The hierarchical relation module mainly captures and aggregates more discriminative relation information among human parts at multiple levels (including the human instance, action region, and body part levels) and objects via a unified graph and exploits a language-guided attentive fusion way to highlight informative features of each interaction level. To address the second issue, we introduce a contrastive learning mechanism to alleviate the inter-class variance. Compared with the previous ProtoNet-based methods, our model generates more discriminative representations for low inter-class variance data, since it makes full use of potential contrastive pairs in each training episode. Extensive experimental results on two standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art FS-HOI methods.