  Large vision-language models have impressively promote the performance of 2D
visual recognition under zero/few-shot scenarios. In this paper, we focus on
exploiting the large vision-language model, i.e., CLIP, to address
zero/few-shot 3D shape recognition based on multi-view representations. The key
challenge for both tasks is to generate a discriminative descriptor of the 3D
shape represented by multiple view images under the scenarios of either without
explicit training (zero-shot 3D shape recognition) or training with a limited
number of data (few-shot 3D shape recognition). We analyze that both tasks are
relevant and can be considered simultaneously. Specifically, leveraging the
descriptor which is effective for zero-shot inference to guide the tuning of
the aggregated descriptor under the few-shot training can significantly improve
the few-shot learning efficacy. Hence, we propose Prompt-Enhanced View
Aggregation Network (PEVA-Net) to simultaneously address zero/few-shot 3D shape
recognition. Under the zero-shot scenario, we propose to leverage the prompts
built up from candidate categories to enhance the aggregation process of
multiple view-associated visual features. The resulting aggregated feature
serves for effective zero-shot recognition of the 3D shapes. Under the few-shot
scenario, we first exploit a transformer encoder to aggregate the
view-associated visual features into a global descriptor. To tune the encoder,
together with the main classification loss, we propose a self-distillation
scheme via a feature distillation loss by treating the zero-shot descriptor as
the guidance signal for the few-shot descriptor. This scheme can significantly
enhance the few-shot learning efficacy.
