A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos

Deep learning models have been widely used for anomaly detection in surveillance videos. Typical models are equipped with the capability to reconstruct normal videos and evaluate the reconstruction errors on anomalous videos to indicate the extent of abnormalities. However, existing approaches suffer from two disadvantages. Firstly, they can only encode the movements of each identity independently, without considering the interactions among identities which may also indicate anomalies. Secondly, they leverage inflexible models whose structures are fixed under different scenes, this configuration disables the understanding of scenes. In this paper, we propose a Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to address these problems, the HSTGCNN is composed of multiple branches that correspond to different levels of graph representations. High-level graph representations encode the trajectories of people and the interactions among multiple identities while low-level graph representations encode the local body postures of each person. Furthermore, we propose to weightedly combine multiple branches that are better at different scenes. An improvement over single-level graph representations is achieved in this way. An understanding of scenes is achieved and serves anomaly detection. High-level graph representations are assigned higher weights to encode moving speed and directions of people in low-resolution videos while low-level graph representations are assigned higher weights to encode human skeletons in high-resolution videos. Experimental results show that the proposed HSTGCNN significantly outperforms current state-of-the-art models on four benchmark datasets (UCSD Pedestrian, ShanghaiTech, CUHK Avenue and IITB-Corridor) by using much less learnable parameters.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT

SPColor: Semantic Prior Guided Exemplar-based Image Colorization

Exemplar-based image colorization aims to colorize a target grayscale image based on a color reference image, and the key is to establish accurate pixel-level semantic correspondence between these two images. Previous methods search for correspondence across the entire reference image, and this type of global matching is easy to get mismatch. We summarize the difficulties in two aspects: (1) When the reference image only contains a part of objects related to target image, improper correspondence will be established in unrelated regions. (2) It is prone to get mismatch in regions where the shape or texture of the object is easily confused. To overcome these issues, we propose SPColor, a semantic prior guided exemplar-based image colorization framework. Different from previous methods, SPColor first coarsely classifies pixels of the reference and target images to several pseudo-classes under the guidance of semantic prior, then the correspondences are only established locally between the pixels in the same class via the newly designed semantic prior guided correspondence network. In this way, improper correspondence between different semantic classes is explicitly excluded, and the mismatch is obviously alleviated. Besides, to better reserve the color from reference, a similarity masked perceptual loss is designed. Noting that the carefully designed SPColor utilizes the semantic prior provided by an unsupervised segmentation model, which is free for additional manual semantic annotations. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively on public dataset

Exemplar-based Video Colorization with Long-term Spatiotemporal Dependency

Exemplar-based video colorization is an essential technique for applications like old movie restoration. Although recent methods perform well in still scenes or scenes with regular movement, they always lack robustness in moving scenes due to their weak ability in modeling long-term dependency both spatially and temporally, leading to color fading, color discontinuity or other artifacts. To solve this problem, we propose an exemplar-based video colorization framework with long-term spatiotemporal dependency. To enhance the long-term spatial dependency, a parallelized CNN-Transformer block and a double head non-local operation are designed. The proposed CNN-Transformer block can better incorporate long-term spatial dependency with local texture and structural features, and the double head non-local operation further leverages the performance of augmented feature. While for long-term temporal dependency enhancement, we further introduce the novel linkage subnet. The linkage subnet propagate motion information across adjacent frame blocks and help to maintain temporal continuity. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively. Also, our model can generate more colorful, realistic and stabilized results, especially for scenes where objects change greatly and irregularly

Zehn Jahre in Deutschland 1935-1945

Ji Xianlin; life; Germany; Göttinge

Erk1 Positively Regulates Osteoclast Differentiation and Bone Resorptive Activity

The extracellular signal-regulated kinases (ERK1 and 2) are widely-expressed and they modulate proliferation, survival, differentiation, and protein synthesis in multiple cell lineages. Altered ERK1/2 signaling is found in several genetic diseases with skeletal phenotypes, including Noonan syndrome, Neurofibromatosis type 1, and Cardio-facio-cutaneous syndrome, suggesting that MEK-ERK signals regulate human skeletal development. Here, we examine the consequence of Erk1 and Erk2 disruption in multiple functions of osteoclasts, specialized macrophage/monocyte lineage-derived cells that resorb bone. We demonstrate that Erk1 positively regulates osteoclast development and bone resorptive activity, as genetic disruption of Erk1 reduced osteoclast progenitor cell numbers, compromised pit formation, and diminished M-CSF-mediated adhesion and migration. Moreover, WT mice reconstituted long-term with Erk1−/− bone marrow mononuclear cells (BMMNCs) demonstrated increased bone mineral density as compared to recipients transplanted with WT and Erk2−/− BMMNCs, implicating marrow autonomous, Erk1-dependent osteoclast function. These data demonstrate Erk1 plays an important role in osteoclast functions while providing rationale for the development of Erk1-specific inhibitors for experimental investigation and/or therapeutic modulation of aberrant osteoclast function

A proteasome-resistant fragment of NIK mediates oncogenic NF-κB signaling in schwannomas

Schwannomas are common, highly morbid and medically untreatable tumors that can arise in patients with germ line as well as somatic mutations in neurofibromatosis type 2 (NF2). These mutations most commonly result in the loss of function of the NF2-encoded protein, Merlin. Little is known about how Merlin functions endogenously as a tumor suppressor and how its loss leads to oncogenic transformation in Schwann cells (SCs). Here, we identify nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)-inducing kinase (NIK) as a potential drug target driving NF-κB signaling and Merlin-deficient schwannoma genesis. Using a genomic approach to profile aberrant tumor signaling pathways, we describe multiple upregulated NF-κB signaling elements in human and murine schwannomas, leading us to identify a caspase-cleaved, proteasome-resistant NIK kinase domain fragment that amplifies pathogenic NF-κB signaling. Lentiviral-mediated transduction of this NIK fragment into normal SCs promotes proliferation, survival, and adhesion while inducing schwannoma formation in a novel in vivo orthotopic transplant model. Furthermore, we describe an NF-κB-potentiated hepatocyte growth factor (HGF) to MET proto-oncogene receptor tyrosine kinase (c-Met) autocrine feed-forward loop promoting SC proliferation. These innovative studies identify a novel signaling axis underlying schwannoma formation, revealing new and potentially druggable schwannoma vulnerabilities with future therapeutic potential

Standardized immunoprecipitation protocol for efficient isolation of native apolipoprotein E particles utilizing HJ15.4 monoclonal antibody

The apolipoprotein E protein (apoE) confers differential risk for Alzheimer\u27s disease depending on which isoforms are expressed. Here, we present a 2-day immunoprecipitation protocol using the HJ15.4 monoclonal apoE antibody for the pull-down of native apoE particles. We describe major steps for apoE production via immortalized astrocyte culture and HJ15.4 antibody bead coupling for apoE particle pull-down, elution, and characterization. This protocol could be used to isolate native apoE particles from multiple model systems or human biospecimens