Semi-Supervised 2D Pose Estimation for Underwater Divers via Limb-Heatmap-Guided Bidirectional Cross-Representation Consistency Learning

Underwater image degradation and occlusion make diver keypoints less visually discernible, leading to substantial localization errors in the keypoint pseudo-labels generated by existing semi-supervised methods. These errors can then accumulate and propagate during consistency learning. Moreover, these methods do not fully exploit the human structural information available in unlabeled images. To address these limitations, a limb-heatmap-guided bidirectional cross-representation consistency method is proposed for semi-supervised learning. A keypoint network and a limb network are employed to learn point-level keypoint responses and region-level limb-structure responses, respectively. For labeled data, limb labels are generated automatically from keypoint annotations and a predefined skeletal topology, without additional annotations. For unlabeled data, intra-representation consistency learning is performed for both networks between the easy-augmentation and hard-augmentation views, and bidirectional cross-representation supervision is applied between the two networks. In the keypoint-to-limb path, keypoint predictions are converted into limb pseudo-labels to supervise the limb network; in the limb-to-keypoint path, limb heatmaps reconstructed from keypoint predictions are compared with the predictions of the limb network to constrain the keypoint configuration. During model inference, only the keypoint network is retained, with no additional inference cost. On the independent test set of a single underwater diver dataset constructed for this study, the proposed method achieves a mean [email protected] of 80.07% using all labeled training data and outperforms the comparison methods at labeled-data proportions of 25%, 50%, 75%, and 100%. These results demonstrate that region-level limb structure provides effective complementary supervision for point-level keypoint localization within the evaluated setting. Broader applicability requires further validation.