LPN: Language-guided Prototypical Network for few-shot classification

Few-shot classification aims to adapt to new tasks with limited labeled examples. To fully use the accessible data, recent methods explore suitable measures for the similarity between the query and support images and better high-dimensional features with meta-training and pre-training strategies. However, the potential of multi-modality information has barely been explored, which may bring promising improvement for few-shot classification. In this paper, we propose a Language-guided Prototypical Network (LPN) for few-shot classification, which leverages the complementarity of vision and language modalities via two parallel branches. Concretely, to introduce language modality with limited samples in the visual task, we leverage a pre-trained text encoder to extract class-level text features directly from class names while processing images with a conventional image encoder. Then, a language-guided decoder is introduced to obtain text features corresponding to each image by aligning class-level features with visual features. In addition, to take advantage of class-level features and prototypes, we build a refined prototypical head that generates robust prototypes in the text branch for follow-up measurement. Finally, we aggregate the visual and text logits to calibrate the deviation of a single modality. Extensive experiments demonstrate the competitiveness of LPN against state-of-the-art methods on benchmark datasets

Multimodal Prototype-Enhanced Network for Few-Shot Action Recognition

Current methods for few-shot action recognition mainly fall into the metric learning framework following ProtoNet. However, they either ignore the effect of representative prototypes or fail to enhance the prototypes with multimodal information adequately. In this work, we propose a novel Multimodal Prototype-Enhanced Network (MORN) to use the semantic information of label texts as multimodal information to enhance prototypes, including two modality flows. A CLIP visual encoder is introduced in the visual flow, and visual prototypes are computed by the Temporal-Relational CrossTransformer (TRX) module. A frozen CLIP text encoder is introduced in the text flow, and a semantic-enhanced module is used to enhance text features. After inflating, text prototypes are obtained. The final multimodal prototypes are then computed by a multimodal prototype-enhanced module. Besides, there exist no evaluation metrics to evaluate the quality of prototypes. To the best of our knowledge, we are the first to propose a prototype evaluation metric called Prototype Similarity Difference (PRIDE), which is used to evaluate the performance of prototypes in discriminating different categories. We conduct extensive experiments on four popular datasets. MORN achieves state-of-the-art results on HMDB51, UCF101, Kinetics and SSv2. MORN also performs well on PRIDE, and we explore the correlation between PRIDE and accuracy