On the Calibration of Human Pose Estimation

Most 2D human pose estimation frameworks estimate keypoint confidence in an ad-hoc manner, using heuristics such as the maximum value of heatmaps. The confidence is part of the evaluation scheme, e.g., AP for the MSCOCO dataset, yet has been largely overlooked in the development of state-of-the-art methods. This paper takes the first steps in addressing miscalibration in pose estimation. From a calibration point of view, the confidence should be aligned with the pose accuracy. In practice, existing methods are poorly calibrated. We show, through theoretical analysis, why a miscalibration gap exists and how to narrow the gap. Simply predicting the instance size and adjusting the confidence function gives considerable AP improvements. Given the black-box nature of deep neural networks, however, it is not possible to fully close this gap with only closed-form adjustments. As such, we go one step further and learn network-specific adjustments by enforcing consistency between confidence and pose accuracy. Our proposed Calibrated ConfidenceNet (CCNet) is a light-weight post-hoc addition that improves AP by up to 1.4% on off-the-shelf pose estimation frameworks. Applied to the downstream task of mesh recovery, CCNet facilitates an additional 1.0mm decrease in 3D keypoint error

Learning Unorthogonalized Matrices for Rotation Estimation

Estimating 3D rotations is a common procedure for 3D computer vision. The accuracy depends heavily on the rotation representation. One form of representation -- rotation matrices -- is popular due to its continuity, especially for pose estimation tasks. The learning process usually incorporates orthogonalization to ensure orthonormal matrices. Our work reveals, through gradient analysis, that common orthogonalization procedures based on the Gram-Schmidt process and singular value decomposition will slow down training efficiency. To this end, we advocate removing orthogonalization from the learning process and learning unorthogonalized `Pseudo' Rotation Matrices (PRoM). An optimization analysis shows that PRoM converges faster and to a better solution. By replacing the orthogonalization incorporated representation with our proposed PRoM in various rotation-related tasks, we achieve state-of-the-art results on large-scale benchmarks for human pose estimation

HLF promotes ovarian cancer progression and chemoresistance via regulating Hippo signaling pathway

Abstract Hepatic leukemia factor (HLF) is aberrantly expressed in human malignancies. However, the role of HLF in the regulation of ovarian cancer (OC) remains unknown. Herein, we reported that HLF expression was upregulated in OC tissues and ovarian cancer stem cells (CSCs). Functional studies have revealed that HLF regulates OC cell stemness, proliferation, and metastasis. Mechanistically, HLF transcriptionally activated Yes-associated protein 1 (YAP1) expression and subsequently modulated the Hippo signaling pathway. Moreover, we found that miR-520e directly targeted HLF 3′-UTR in OC cells. miR-520e expression was negatively correlated with HLF and YAP1 expression in OC tissues. The combined immunohistochemical (IHC) panels exhibited a better prognostic value for OC patients than any of these components alone. Importantly, the HLF/YAP1 axis determines the response of OC cells to carboplatin treatment and HLF depletion or the YAP1 inhibitor verteporfin abrogated carboplatin resistance. Analysis of patient-derived xenografts (PDXs) further suggested that HLF might predict carboplatin benefits in OC patients. In conclusion, these findings suggest a crucial role of the miR-520e/HLF/YAP1 axis in OC progression and chemoresistance, suggesting potential therapeutic targets for OC