Online Metric-Weighted Linear Representations for Robust Visual Tracking

In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned metric is then incorporated into a linear representation of appearance. We show that online distance metric learning significantly improves the robustness of the tracker, especially on those sequences exhibiting drastic appearance changes. In order to bound growth in the number of training samples, we design a time-weighted reservoir sampling method. Moreover, we enable our tracker to automatically perform object identification during the process of object tracking, by introducing a collection of static template samples belonging to several object classes of interest. Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame. Experimental results on challenging video sequences demonstrate the effectiveness of the method for both inter-frame tracking and object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and Machine Intelligenc

Receptor Determinants And Entry Pathways Of Coronavirus And Influenza Virus

Avian coronavirus infectious bronchitis virus (IBV) and influenza virus are two respiratory viruses that have great impact on veterinary and human health globally. The two respiratory viruses described in this thesis share many commonalities on the molecular mechanisms of entry in host cells. Following cell surface attachment via receptor binding, these enveloped viruses need to be internalized into subcellular compartments to initiate fusion and uncoating. Although often studied in certain prototypical cell types, many details of attachment and entry pathways are overlooked in more in vivo-relevant cell types. Despite many speculations on its receptor usage, the authentic IBV receptor has not been discovered although the attachment factor sialic acid is documented. When I expressed DC-SIGN and L-SIGN, the C-type lectins, in mammalian cells, I discovered that IBV are able to infect these non-permissive cells that are usually refractory to IBV infection. In addition, the infection in DC-SIGN-expressing cells is independent on the level of sialic acid on the cell surface. When I examed whether sialic acid also plays a role in IBV infection of chicken peripheral blood derived monocytes (chPBMCs), cells that potentially harbor the authentic proteinaceous receptor for IBV, I unexpectedly found that the established attachment factor sialic acid does not play a critical role in the IBV infection in chPBMC. To further evaluate the entry pathways utilized by IBV in chPBMCs, I examined the IBV infection in the presence chemical inhibitors used to disrupt endocytic components. Using immunofluorescence microscopy, I identified that caveolae-dependent endocytosis and macropinocytosis pathways were used by IBV entry in chPBMCs. In a similar chemical and molecular approach, in the presence of endocytic inhibitors and dominant- negative proteins implicated in the endocytic pathways, I evaluated the influenza virus entry in polarized epithelial cells-- Madin-Darby canine kidney (MDCK) cells, a model for the cells at primary sites of influenza infection in vivo. My study showed that in polarized MDCK II cells, influenza virus has a differential utilization for CME pathway and requires Eps15 protein for entry In all, these studies of coronavirus and influenza virus entry provide a clearer understanding of the molecular mechanisms involved in enveloped virus entry into host cells. The results of this study will enrich our knowledge of enveloped virus pathogenesis

Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.Comment: AAAI 201

How Cultural Factors Affect Chinese Americans’ Attitudes Towards Seeking Mental Health Services

Chinese Americans are the largest ethnic group among Asian Americans. However, the treatment rate for mental illness among Chinese Americans is much lower compared to other ethnic groups. Studies have been conducted on cultural barriers that prevent Asian Americans from seeking mental health treatment, but there is a lack of research on specific ethnic groups, such as Chinese Americans or Korean Americans because they are frequently grouped into homogenous clusters. This study will identify the cultural factors that influence Chinese Americans’ attitudes towards seeking mental health treatment and analyze how these factors affect their behaviors in seeking mental health treatment

Consensus Graph Representation Learning for Better Grounded Image Captioning

The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words. The most common way is to encourage the captioning model to dynamically link generated object words or phrases to appropriate regions of the image, i.e., the grounded image captioning (GIC). However, GIC utilizes an auxiliary task (grounding objects) that has not solved the key issue of object hallucination, i.e., the semantic inconsistency. In this paper, we take a novel perspective on the issue above - exploiting the semantic coherency between the visual and language modalities. Specifically, we propose the Consensus Rraph Representation Learning framework (CGRL) for GIC that incorporates a consensus representation into the grounded captioning pipeline. The consensus is learned by aligning the visual graph (e.g., scene graph) to the language graph that consider both the nodes and edges in a graph. With the aligned consensus, the captioning model can capture both the correct linguistic characteristics and visual relevance, and then grounding appropriate image regions further. We validate the effectiveness of our model, with a significant decline in object hallucination (-9% CHAIRi) on the Flickr30k Entities dataset. Besides, our CGRL also evaluated by several automatic metrics and human evaluation, the results indicate that the proposed approach can simultaneously improve the performance of image captioning (+2.9 Cider) and grounding (+2.3 F1LOC).Comment: 9 pages, 5 figures, AAAI 202