Granularity-Aware Contrastive Learning for Fine-Grained Action Recognition

The contrastive learning paradigm has been widely used for image-language pre-training and extended to video-text tuning. These approaches aim to maximize the similarity between positive sample pairs while minimizing that of negative ones through an alignment objective. Their performance is highly affected by the definition of positive and negative pairs which depends on the granularity of label classification. This effect is particularly apparent in video action recognition, where different fine-grained actions may belong to a shared coarse label. Therefore, indiscriminately treating a video sample and labels that are not identical at the fine-grained level but share the same coarse label as negative pairs leads to pushing the sample apart from the cluster of its basic coarse action. Such conflict can potentially prevent the model from pulling the sample and its target label closer. For a balanced understanding of coarse and fine-grained distinctions, we propose the Granularity-Aware Contrastive Learning (GACon) framework to improve contrastive learning for fine-grained action recognition. This is achieved through (i) a refined definition of sample-label relation and alignment objectives, and (ii) the exchange of coarse and fine-grained information between two granularity-distinct experts. Experiments on four benchmarks of fine-grained action recognition show the superiority of our proposed GACon compared to existing approaches.