Confidence Self-Calibration for Multi-Label Class-Incremental Learning
Kaile Du Yifan Zhou Fan Lyu Yuyang Li Chen Lu Guangcan Liu
The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL)
arises when only the new classes are labeled during training, while past and
future labels remain unavailable. This issue leads to a proliferation of
false-positive errors due to erroneously high confidence multi-label
predictions, exacerbating catastrophic forgetting within the disjoint label
space. In this paper, we aim to refine multi-label confidence calibration in
MLCIL and propose a Confidence Self-Calibration (CSC) approach. Firstly, for
label relationship calibration, we introduce a class-incremental graph
convolutional network that bridges the isolated label spaces by constructing
learnable, dynamically extended label relationship graph. Then, for confidence
calibration, we present a max-entropy regularization for each multi-label
increment, facilitating confidence self-calibration through the penalization of
over-confident output distributions. Our approach attains new state-of-the-art
results in MLCIL tasks on both MS-COCO and PASCAL VOC datasets, with the
calibration of label confidences confirmed through our methodology.