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