  Bottom-up based multi-person pose estimation approaches use heatmaps with
auxiliary predictions to estimate joint positions and belonging at one time.
Recently, various combinations between auxiliary predictions and heatmaps have
been proposed for higher performance, these predictions are supervised by the
corresponding L2 loss function directly. However, the lack of more explicit
supervision results in low features utilization and contradictions between
predictions in one model. To solve these problems, this paper proposes (i) a
new loss organization method which uses self-supervised heatmaps to reduce
prediction contradictions and spatial-sequential attention to enhance networks'
features extraction; (ii) a new combination of predictions composed by
heatmaps, Part Affinity Fields (PAFs) and our block-inside offsets to fix
pixel-level joints positions and further demonstrates the effectiveness of
proposed loss function. Experiments are conducted on the MS COCO keypoint
dataset and adopting OpenPose as the baseline model. Our method outperforms the
baseline overall. On the COCO verification dataset, the mAP of OpenPose trained
with our proposals outperforms the OpenPose baseline by over 5.5%.
