Induction of transcription factor Egr-1 gene expression in astrocytoma cells by Murine coronavirus infection

AbstractMouse hepatitis virus (MHV) causes encephalitis and demyelination in the central nervous system (CNS) of susceptible rodents. Astrocytes are one of the major targets for MHV infection in the CNS, and respond to MHV infection by expressing diverse molecules that may contribute to CNS pathogenesis. Here we characterized the activation of an immediate-early transcription factor Egr-1 by MHV infection in an astrocytoma cell line. We found that the expression of Egr-1 was dramatically increased following virus infection. Using various inhibitors of mitogen-activated protein kinases, we identified that the extracellular signal-regulated kinases 1/2 were involved in the activation of Egr-1 transcription by MHV infection. Experiments with ultraviolet light-inactivated virus revealed that the induction of Egr-1 did not require virus replication and was likely mediated during cell entry. We further found that over-expression of Egr-1 suppressed the expression of BNip3, a pro-apoptotic member of the Bcl-2 family. This finding may provide an explanation for our previously observed down-regulation of BNip3 by MHV infection in astrocytoma cells (Cai, Liu, Yu, and Zhang, Virology 316:104–115, 2003). Furthermore, knockdown of Egr-1 by an siRNA inhibited MHV propagation, suggesting the biological relevance of Egr-1 induction to virus replication. In addition, the persistence/demylinating-positive strains (JHM and A59) induced Egr-1 expression, whereas the persistence/demylinating-negative strain (MHV-2) did not. These results indicate a correlation between the ability of MHVs to induce Egr-1 expression and their ability to cause demyelination in the CNS, which may suggest a potential role for the induction of Egr-1 in viral pathogenesis

Indoor Scene Parsing with Instance Segmentation, Semantic Labeling and Support Relationship Inference

Over the years, indoor scene parsing has attracted a growing interest in the computer vision community. Existing methods have typically focused on diverse subtasks of this challenging problem. In particular, while some of them aim at segmenting the image into regions, such as object instances, others aim at inferring the semantic labels of given regions, or their support relationships. These different tasks are typically treated as separate ones. However, they bear strong connections: good regions should respect the semantic labels; support can only be defined for meaningful regions; support relationships strongly depend on semantics. In this paper, we, therefore, introduce an approach to jointly segment the object instances and infer their semantic labels and support relationships from a single input image. By exploiting a hierarchical segmentation, we formulate our problem as that of jointly finding the regions in the hierarchy that correspond to instances and estimating their class labels and pairwise support relationships. We express this via a Markov Random Field, which allows us to further encode links between the different types of variables. Inference in this model can be done exactly via integer linear programming, and we learn its parameters in a structural SVM framework. Our experiments on NYUv2 demonstrate the benefits of reasoning jointly about all these subtasks of indoor scene parsing.Chinese Scholarship Council; CSIRO-Data61

Part-aware Prototype Network for Few-shot Semantic Segmentation

Few-shot semantic segmentation aims to learn to segment new object classes with only a few annotated examples, which has a wide range of real-world applications. Most existing methods either focus on the restrictive setting of one-way few-shot segmentation or suffer from incomplete coverage of object regions. In this paper, we propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes, capable of capturing diverse and fine-grained object features. In addition, we propose to leverage unlabeled data to enrich our part-aware prototypes, resulting in better modeling of intra-class variations of semantic objects. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes based on labeled and unlabeled images. Extensive experimental evaluations on two benchmarks show that our method outperforms the prior art with a sizable margin.Comment: ECCV-202

3D Box Proposals from a Single Monocular Image of an Indoor Scene

Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bound- ing boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth- based 3D proposal generation.Chinese Scholarship Council; CSIRO-Data61; The Program of Shanghai Subject Chief Scientist (A type) (No.15XD1502900)

HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models

Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation. However, such approaches often rely on large-scale training data and suffer from inferior performance under few/zero-shot scenarios. In this paper, we propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization. In detail, we first introduce a novel interaction decoder to extract informative regions in the visual feature map of CLIP via a cross-attention mechanism, which is then fused with the detection backbone by a knowledge integration block for more accurate human-object pair detection. In addition, prior knowledge in CLIP text encoder is leveraged to generate a classifier by embedding HOI descriptions. To distinguish fine-grained interactions, we build a verb classifier from training data via visual semantic arithmetic and a lightweight verb representation adapter. Furthermore, we propose a training-free enhancement to exploit global HOI predictions from CLIP. Extensive experiments demonstrate that our method outperforms the state of the art by a large margin on various settings, e.g. +4.04 mAP on HICO-Det. The source code is available in https://github.com/Artanic30/HOICLIP.Comment: CVPR 2023.Open sourced, Code and Model Availabl

Learning Cross-modal Context Graph for Visual Grounding

Visual grounding is a ubiquitous building block in many vision-language tasks and yet remains challenging due to large variations in visual and linguistic features of grounding entities, strong context effect and the resulting semantic ambiguities. Prior works typically focus on learning representations of individual phrases with limited context information. To address their limitations, this paper proposes a language-guided graph representation to capture the global context of grounding entities and their relations, and develop a cross-modal graph matching strategy for the multiple-phrase visual grounding task. In particular, we introduce a modular graph neural network to compute context-aware representations of phrases and object proposals respectively via message propagation, followed by a graph-based matching module to generate globally consistent localization of grounding phrases. We train the entire graph neural network jointly in a two-stage strategy and evaluate it on the Flickr30K Entities benchmark. Extensive experiments show that our method outperforms the prior state of the arts by a sizable margin, evidencing the efficacy of our grounding framework. Code is available at "https://github.com/youngfly11/LCMCG-PyTorch".Comment: AAAI-202