C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets

Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classifier for maximum predictive efficiency, Conformal Training rectifies the training objective with a regularization that minimizes the average prediction set size at a specific error rate. However, the regularization term inevitably deteriorates the classification accuracy and leads to suboptimal efficiency of conformal predictors. To address this issue, we introduce \textbf{Conformal Adapter} (C-Adapter), an adapter-based tuning method to enhance the efficiency of conformal predictors without sacrificing accuracy. In particular, we implement the adapter as a class of intra order-preserving functions and tune it with our proposed loss that maximizes the discriminability of non-conformity scores between correctly and randomly matched data-label pairs. Using C-Adapter, the model tends to produce extremely high non-conformity scores for incorrect labels, thereby enhancing the efficiency of prediction sets across different coverage rates. Extensive experiments demonstrate that C-Adapter can effectively adapt various classifiers for efficient prediction sets, as well as enhance the conformal training method.

Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification

Hyperspectral image (HSI) classification involves assigning specific labels to each pixel to identify various land cover categories. Although deep classifiers have shown high predictive accuracy in this field, quantifying their uncertainty remains a significant challenge, which hinders their application in critical contexts. This study first theoretically evaluates the applicability of \textit{Conformal Prediction} (CP), an emerging technique for uncertainty quantification, in the context of HSI classification. We then propose a conformal procedure that provides HSI classifiers with trustworthy prediction sets, offering coverage guarantees that ensure these sets contain the true labels with a user-specified probability. Building on this foundation, we introduce \textit{Spatial-Aware Conformal Prediction} (\texttt{SACP}), which incorporates essential spatial information inherent in HSIs by aggregating non-conformity scores of pixels with high spatial correlation. Both theoretical and empirical results demonstrate that \texttt{SACP} outperforms standard CP in HSI classification. The source code is accessible at \url{https://github.com/J4ckLiu/SACP}.

Boosting Few-Shot Semantic Segmentation Via Segment Anything Model

In semantic segmentation, accurate prediction masks are crucial for downstream tasks such as medical image analysis and image editing. Due to the lack of annotated data, few-shot semantic segmentation (FSS) performs poorly in predicting masks with precise contours. Recently, we have noticed that the large foundation model segment anything model (SAM) performs well in processing detailed features. Inspired by SAM, we propose FSS-SAM to boost FSS methods by addressing the issue of inaccurate contour. The FSS-SAM is training-free. It works as a post-processing tool for any FSS methods and can improve the accuracy of predicted masks. Specifically, we use predicted masks from FSS methods to generate prompts and then use SAM to predict new masks. To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm. The algorithm can remarkably decrease wrong predictions. Experiment results on public datasets show that our method is superior to base FSS methods in both quantitative and qualitative aspects.