8 research outputs found
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