Lidar Point Cloud Guided Monocular 3D Object Detection

Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the detector can achieve very close accuracy compared to the one using the ground-truth labels. We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels. Motivated by the conclusions above and considering the precise LiDAR 3D measurement, we propose a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in 3D space, such pseudo labels can replace manually annotated labels in the training of monocular 3D detectors, since their 3D location information is precise. LPCG can be applied into any monocular 3D detector to fully use massive unlabeled data in a self-driving system. As a result, in KITTI benchmark, we take the first place on both monocular 3D and BEV (bird's-eye-view) detection with a significant margin. In Waymo benchmark, our method using 10% labeled data achieves comparable accuracy to the baseline detector using 100% labeled data. The codes are released at https://github.com/SPengLiang/LPCG.Comment: ECCV 202

Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection

Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when generalized to an unseen manipulation. Some recent works show improvement in generalization but rely on features fragile to image distortions such as compression. To this end, we propose Diff-ID, a concise and effective approach that explains and measures the identity loss induced by facial manipulations. When testing on an image of a specific person, Diff-ID utilizes an authentic image of that person as a reference and aligns them to the same identity-insensitive attribute feature space by applying a face-swapping generator. We then visualize the identity loss between the test and the reference image from the image differences of the aligned pairs, and design a custom metric to quantify the identity loss. The metric is then proved to be effective in distinguishing the forgery images from the real ones. Extensive experiments show that our approach achieves high detection performance on DeepFake images and state-of-the-art generalization ability to unknown forgery methods, while also being robust to image distortions

A quantitative study of salinity effect on water diffusion in n-alkane phases: From pore-scale experiments to molecular dynamic simulation

Numerous mechanisms have been proposed to untangle the effect of a low concentration of dissolved salts in the water flooding medium. One potential mechanism for enhanced oil movement is proposed with osmosis effect, however, the process of water transport through the oil phase, due to a salinity contrast, is not fully understood. In our study, we used three aqueous solutions and two alkanes in a series of microfluidic experiments with hydrophobically coated glass micro-chips for mimicking the low-salinity waterflooding process in an oil-wet rock formation. We created multiple systems of low-salinity water-alkane/high-salinity water in the porous micromodel, and afterward, continuously monitored the domain for 70 h. We noted that ionic strength and the hydrocarbon chain length both played important roles in water diffusion. A salinity contrast of 1.7 g/L-170 g/L caused a higher water volumetric flux than 50 g/L-170 g/L for both alkanes. The difference in water volumetric fluxes for those two contrasts were not proportional to the salinity contrast during the experimental period. There was no simple relationship between the chain length of hydrocarbon and water volumetric flux. Moreover, to investigate the effect of salinity on water behavior in heptane, we conducted molecular dynamic (MD) simulations by considering three different concentrations in the high-salinity water region featuring our experiments. The results indicated that high salinity limited the water diffusion from high-salinity phase into the oil phase and reduced the possibility of water entering the heptane phase. Therefore, the net flux of water from the pure water side to the salty waterside was enhanced

Deeper and Mixed Supervision for Salient Object Detection in Automated Surface Inspection

In recent years, researches in the field of salient object detection have been widely made in many industrial visual inspection tasks. Automated surface inspection (ASI) can be regarded as one of the most challenging tasks in computer vision because of its high cost of data acquisition, serious imbalance of test samples, and high real-time requirement. Inspired by the requirements of industrial ASI and the methods of salient object detection (SOD), a task mode of defect type classification plus defect area segmentation and a novel deeper and mixed supervision network (DMS) architecture is proposed. The backbone network ResNeXt-101 was pretrained on ImageNet. Firstly, we extract five multiscale feature maps from backbone and concatenate them layer by layer. In addition, to obtain the classification prediction and saliency maps in one stage, the image-level and pixel-level ground truth is trained in a same side output network. Supervision signal is imposed on each side layer to realize deeper and mixed training for the network. Furthermore, the DMS network is equipped with residual refinement mechanism to refine the saliency maps of input images. We evaluate the DMS network on 4 open access ASI datasets and compare it with other 20 methods, which indicates that mixed supervision can significantly improve the accuracy of saliency segmentation. Experiment results show that the proposed method can achieve the state-of-the-art performance

HYBID in osteoarthritis: Potential target for disease progression

HYBID is a new hyaluronan-degrading enzyme and exists in various cells of the human body. Recently, HYBID was found to over-express in the osteoarthritic chondrocytes and fibroblast-like synoviocytes. According to these researches, high level of HYBID is significantly correlated with cartilage degeneration in joints and hyaluronic acid degradation in synovial fluid. In addition, HYBID can affect inflammatory cytokine secretion, cartilage and synovium fibrosis, synovial hyperplasia via multiple signaling pathways, thereby exacerbating osteoarthritis. Based on the existing research of HYBID in osteoarthritis, HYBID can break the metabolic balance of HA in joints through the degradation ability independent of HYALs/CD44 system and furthermore affect cartilage structure and mechanotransduction of chondrocytes. In particular, in addition to HYBID itself being able to trigger some signaling pathways, we believe that low-molecular-weight hyaluronan produced by excess degradation can also stimulate some disease-promoting signaling pathways by replacing high-molecular-weight hyaluronan in joints. The specific role of HYBID in osteoarthritis is gradually revealed, and the discovery of HYBID raises the new way to treat osteoarthritis. In this review, the expression and basic functions of HYBID in joints were summarized, and reveal potential role of HYBID as a key target in treatment for osteoarthritis

A quantitative study of salinity effect on water diffusion in n-alkane phases: From pore-scale experiments to molecular dynamic simulation

Numerous mechanisms have been proposed to untangle the effect of a low concentration of dissolved salts in the water flooding medium. One potential mechanism for enhanced oil movement is proposed with osmosis effect, however, the process of water transport through the oil phase, due to a salinity contrast, is not fully understood. In our study, we used three aqueous solutions and two alkanes in a series of microfluidic experiments with hydrophobically coated glass micro-chips for mimicking the low-salinity waterflooding process in an oil-wet rock formation. We created multiple systems of low-salinity water-alkane/high-salinity water in the porous micromodel, and afterward, continuously monitored the domain for 70 h. We noted that ionic strength and the hydrocarbon chain length both played important roles in water diffusion. A salinity contrast of 1.7 g/L-170 g/L caused a higher water volumetric flux than 50 g/L-170 g/L for both alkanes. The difference in water volumetric fluxes for those two contrasts were not proportional to the salinity contrast during the experimental period. There was no simple relationship between the chain length of hydrocarbon and water volumetric flux. Moreover, to investigate the effect of salinity on water behavior in heptane, we conducted molecular dynamic (MD) simulations by considering three different concentrations in the high-salinity water region featuring our experiments. The results indicated that high salinity limited the water diffusion from high-salinity phase into the oil phase and reduced the possibility of water entering the heptane phase. Therefore, the net flux of water from the pure water side to the salty waterside was enhanced

HSP90 C-terminal domain inhibition promotes VDAC1 oligomerization via decreasing K274 mono-ubiquitination in Hepatocellular Carcinoma

Voltage-dependent anion-selective channel protein 1 (VDAC1) is the most abundant protein in the mitochondrial outer membrane and plays a crucial role in the control of hepatocellular carcinoma (HCC) progress. Our previous research found that cytosolic molecular chaperone heat shock protein 90 (Hsp90) interacted with VDAC1, but the effect of the C-terminal and N-terminal domains of Hsp90 on the formation of VDAC1 oligomers is unclear. In this study, we focused on the effect of the C-terminal domain of Hsp90 on VDAC1 oligomerization, ubiquitination, and VDAC1 channel activity. We found that Hsp90 C-terminal domain inhibitor Novobiocin promoted VDAC1 oligomerization, release of cytochrome c, and activated mitochondrial apoptosis pathway. Atomic coarse particle modeling simulation revealed C-terminal domain of Hsp90α stabilized VDAC1 monomers. The purified VDAC1 was reconstituted into a planar lipid bilayer, and electrophysiology experiments of patch clamp showed that the Hsp90 C-terminal inhibitor Novobiocin increased VDAC1 channel conductance via promoting VDAC1 oligomerization. The mitochondrial ubiquitination proteomics results showed that VDAC1 K274 mono-ubiquitination was significantly decreased upon Novobiocin treatment. Site-directed mutation of VDAC1 (K274R) weakened Hsp90α-VDAC1 interaction and increased VDAC1 oligomerization. Taken together, our results reveal that Hsp90 C-terminal domain inhibition promotes VDAC1 oligomerization and VDAC1 channel conductance by decreasing VDAC1 K274 mono- ubiquitination, which provides a new perspective for mitochondria-targeted therapy of HCC