A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis

Few-shot text classification has attracted great interest in both academia and industry due to the lack of labeled data in many fields. Different from general text classification (e.g., topic classification), few-shot sentiment classification is more challenging because the semantic distances among the classes are more subtle. For instance, the semantic distances between the sentiment labels in a positive or negative polarity (e.g., ``love" and ``joy", ``remorse" and ``sadness") are close, while the distances are large for the sentiment labels in two opposite polarities (e.g., ``love" and ``sadness"). To address this problem, we propose a Soft Contrastive learning-based Prompt (\texttt{SCP}) model for few-shot sentiment analysis. First, we design a sentiment-aware chain of thought prompt module to guide the model to predict the sentiment from coarse grain to fine grain via a series of intermediate reasoning steps. Then, we propose a soft contrastive learning algorithm to take the correlation of the labels into account. A series of experiments on several sentiment analysis datasets show the great advantages of \texttt{SCP} by comparing it with SOTA baselines (e.g., ChatGPT).

When Hyperspectral Image Classification Meets Diffusion Models: An Unsupervised Feature Learning Framework

Learning effective spectral-spatial features is important for the hyperspectral image (HSI) classification task, but the majority of existing HSI classification methods still suffer from modeling complex spectral-spatial relations and characterizing low-level details and high-level semantics comprehensively. As a new class of record-breaking generative models, diffusion models are capable of modeling complex relations for understanding inputs well as learning both high-level and low-level visual features. Meanwhile, diffusion models can capture more abundant features by taking advantage of the extra and unique dimension of timestep t. In view of these, we propose an unsupervised spectral-spatial feature learning framework based on the diffusion model for HSI classification for the first time, named Diff-HSI. Specifically, we first pretrain the diffusion model with unlabeled HSI patches for unsupervised feature learning, and then exploit intermediate hierarchical features from different timesteps for classification. For better using the abundant timestep-wise features, we design a timestep-wise feature bank and a dynamic feature fusion module to construct timestep-wise features, adaptively learning informative multi-timestep representations. Finally, an ensemble of linear classifiers is applied to perform HSI classification. Extensive experiments are conducted on three public HSI datasets, and our results demonstrate that Diff-HSI outperforms state-of-the-art supervised and unsupervised methods for HSI classification.