353 research outputs found
Deep Image Harmonization
Compositing is one of the most common operations in photo editing. To
generate realistic composites, the appearances of foreground and background
need to be adjusted to make them compatible. Previous approaches to harmonize
composites have focused on learning statistical relationships between
hand-crafted appearance features of the foreground and background, which is
unreliable especially when the contents in the two layers are vastly different.
In this work, we propose an end-to-end deep convolutional neural network for
image harmonization, which can capture both the context and semantic
information of the composite images during harmonization. We also introduce an
efficient way to collect large-scale and high-quality training data that can
facilitate the training process. Experiments on the synthesized dataset and
real composite images show that the proposed network outperforms previous
state-of-the-art methods
Re-Benchmarking Pool-Based Active Learning for Binary Classification
Active learning is a paradigm that significantly enhances the performance of
machine learning models when acquiring labeled data is expensive. While several
benchmarks exist for evaluating active learning strategies, their findings
exhibit some misalignment. This discrepancy motivates us to develop a
transparent and reproducible benchmark for the community. Our efforts result in
an open-sourced implementation
(https://github.com/ariapoy/active-learning-benchmark) that is reliable and
extensible for future research. By conducting thorough re-benchmarking
experiments, we have not only rectified misconfigurations in existing benchmark
but also shed light on the under-explored issue of model compatibility, which
directly causes the observed discrepancy. Resolving the discrepancy reassures
that the uncertainty sampling strategy of active learning remains an effective
and preferred choice for most datasets. Our experience highlights the
importance of dedicating research efforts towards re-benchmarking existing
benchmarks to produce more credible results and gain deeper insights
Dysregulated Apoptosis Through the Intrinsic Pathway in the Internal Spermatic Vein of Patients With Varicocele
Background/PurposeApoptosis plays a critical role in various physiological processes. Varicocele is the most common cause of male infertility in adults. The dilated and thickened wall of the internal spermatic vein (ISV) in varicocele is considered similar to that in varicose veins. We investigated apoptotic protein expression in the ISV of varicocele, including Bcl-2, Fas, caspase-8 and caspase-9, to determine the intrinsic or extrinsic pathway.MethodsThe study group consisted of 10 patients with grade 3 left varicocele. The control group consisted of 10 patients with left indirect inguinal hernia. A 1-cm section of ISV was resected, using left inguinal incision, from each patient in both groups. The ISV sections were used to detect the mediators that regulate the intrinsic (Bcl-2 and caspase-9) and extrinsic (Fas and caspase-8) apoptotic pathways, by immunoblotting and immunohistochemical staining. Results were analyzed using Student's t tests.ResultsBcl-2, Fas, caspase-8 and caspase-9 immunoblots from both groups revealed a single band. The relative intensities of the Bcl-2 and caspase-9 protein bands differed significantly between the varicocele and control groups. Thickening of the smooth muscle layer of the ISV was found in patients with varicocele compared with the control group. Bcl-2 overexpression and downregulation of caspase-9 expression were noted in the varicocele group. There was no significant difference in Fas or caspase-8 expression in either group.ConclusionWe showed overexpression of Bcl-2 and downregulation of caspase-9 expression in the ISV under hypoxic stress. This indicated dysregulated apoptosis through the intrinsic pathway in the ISV of patients with varicocele. To the best of our knowledge, this is the first study of the apoptotic pathway in the human ISV. Additional studies are needed to establish whether adjunctive hyperbaric oxygen therapy reduces the recurrence rate after varicocelectomy
3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling
For monocular depth estimation, acquiring ground truths for real data is not
easy, and thus domain adaptation methods are commonly adopted using the
supervised synthetic data. However, this may still incur a large domain gap due
to the lack of supervision from the real data. In this paper, we develop a
domain adaptation framework via generating reliable pseudo ground truths of
depth from real data to provide direct supervisions. Specifically, we propose
two mechanisms for pseudo-labeling: 1) 2D-based pseudo-labels via measuring the
consistency of depth predictions when images are with the same content but
different styles; 2) 3D-aware pseudo-labels via a point cloud completion
network that learns to complete the depth values in the 3D space, thus
providing more structural information in a scene to refine and generate more
reliable pseudo-labels. In experiments, we show that our pseudo-labeling
methods improve depth estimation in various settings, including the usage of
stereo pairs during training. Furthermore, the proposed method performs
favorably against several state-of-the-art unsupervised domain adaptation
approaches in real-world datasets.Comment: Accepted in ECCV 2022. Project page:
https://ccc870206.github.io/3D-PL
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