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

Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation