  Modern visual recognition models often display overconfidence due to their
reliance on complex deep neural networks and one-hot target supervision,
resulting in unreliable confidence scores that necessitate calibration. While
current confidence calibration techniques primarily address single-label
scenarios, there is a lack of focus on more practical and generalizable
multi-label contexts. This paper introduces the Multi-Label Confidence
Calibration (MLCC) task, aiming to provide well-calibrated confidence scores in
multi-label scenarios. Unlike single-label images, multi-label images contain
multiple objects, leading to semantic confusion and further unreliability in
confidence scores. Existing single-label calibration methods, based on label
smoothing, fail to account for category correlations, which are crucial for
addressing semantic confusion, thereby yielding sub-optimal performance. To
overcome these limitations, we propose the Dynamic Correlation Learning and
Regularization (DCLR) algorithm, which leverages multi-grained semantic
correlations to better model semantic confusion for adaptive regularization.
DCLR learns dynamic instance-level and prototype-level similarities specific to
each category, using these to measure semantic correlations across different
categories. With this understanding, we construct adaptive label vectors that
assign higher values to categories with strong correlations, thereby
facilitating more effective regularization. We establish an evaluation
benchmark, re-implementing several advanced confidence calibration algorithms
and applying them to leading multi-label recognition (MLR) models for fair
comparison. Through extensive experiments, we demonstrate the superior
performance of DCLR over existing methods in providing reliable confidence
scores in multi-label scenarios.
