Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification

Spatial Pyramid Matching (SPM) and its variants have achieved a lot of success in image classification. The main difference among them is their encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of Vector Quantization (VQ) into the framework of SPM. Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image. In this paper, we propose using Low Rank Representation (LRR) to encode the descriptors under the framework of SPM. Different from SC, LRR considers the group effect among data points instead of sparsity. Benefiting from this property, the proposed method (i.e., LrrSPM) can offer a better performance. To further improve the generalizability and robustness, we reformulate the rank-minimization problem as a truncated projection problem. Extensive experimental studies show that LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201

SENP3-mediated host defense response contains HBV replication and restores protein synthesis

Certain organs are capable of containing the replication of various types of viruses. In the liver, infection of Hepatitis B virus (HBV), the etiological factor of Hepatitis B and hepatocellular carcinoma (HCC), often remains asymptomatic and leads to a chronic carrier state. Here we investigated how hepatocytes contain HBV replication and promote their own survival by orchestrating a translational defense mechanism via the stress-sensitive SUMO-2/3-specific peptidase SENP3. We found that SENP3 expression level decreased in HBV-infected hepatocytes in various models including HepG2-NTCP cell lines and a humanized mouse model. Downregulation of SENP3 reduced HBV replication and boosted host protein translation. We also discovered that IQGAP2, a Ras GTPase-activating-like protein, is a key substrate for SENP3-mediated de-SUMOylation. Downregulation of SENP3 in HBV infected cells facilitated IQGAP2 SUMOylation and degradation, which leads to suppression of HBV gene expression and restoration of global translation of host genes via modulation of AKT phosphorylation. Thus, The SENP3-IQGAP2 de-SUMOylation axis is a host defense mechanism of hepatocytes that restores host protein translation and suppresses HBV gene expression

3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization

Although deep-learning based methods for monocular pedestrian detection have made great progress, they are still vulnerable to heavy occlusions. Using multi-view information fusion is a potential solution but has limited applications, due to the lack of annotated training samples in existing multi-view datasets, which increases the risk of overfitting. To address this problem, a data augmentation method is proposed to randomly generate 3D cylinder occlusions, on the ground plane, which are of the average size of pedestrians and projected to multiple views, to relieve the impact of overfitting in the training. Moreover, the feature map of each view is projected to multiple parallel planes at different heights, by using homographies, which allows the CNNs to fully utilize the features across the height of each pedestrian to infer the locations of pedestrians on the ground plane. The proposed 3DROM method has a greatly improved performance in comparison with the state-of-the-art deep-learning based methods for multi-view pedestrian detection

3D Random Occlusion and Multi-layer Projection for Deep Multi-camera Pedestrian Localization

Although deep-learning based methods for monocular pedestrian detection have made great progress, they are still vulnerable to heavy occlusions. Using multi-view information fusion is a potential solution but has limited applications, due to the lack of annotated training samples in existing multi-view datasets, which increases the risk of overfitting. To address this problem, a data augmentation method is proposed to randomly generate 3D cylinder occlusions, on the ground plane, which are of the average size of pedestrians and projected to multiple views, to relieve the impact of overfitting in the training. Moreover, the feature map of each view is projected to multiple parallel planes at different heights, by using homographies, which allows the CNNs to fully utilize the features across the height of each pedestrian to infer the locations of pedestrians on the ground plane. The proposed 3DROM method has a greatly improved performance in comparison with the state-of-the-art deep-learning based methods for multi-view pedestrian detection. Code is available at https://github.com/xjtlu-cvlab/3DROM