Weakly Supervised Gaussian Networks for Action Detection

Detecting temporal extents of human actions in videos is a challenging computer vision problem that requires detailed manual supervision including frame-level labels. This expensive annotation process limits deploying action detectors to a limited number of categories. We propose a novel method, called WSGN, that learns to detect actions from \emph{weak supervision}, using only video-level labels. WSGN learns to exploit both video-specific and dataset-wide statistics to predict relevance of each frame to an action category. This strategy leads to significant gains in action detection for two standard benchmarks THUMOS14 and Charades. Our method obtains excellent results compared to state-of-the-art methods that uses similar features and loss functions on THUMOS14 dataset. Similarly, our weakly supervised method is only 0.3% mAP behind a state-of-the-art supervised method on challenging Charades dataset for action localization.Comment: Accepted in WACV 202

Towards a unified account of face (and maybe object) processing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 191-197).Faces are an important class of visual stimuli, and are thought to be processed differently from objects by the human visual system. Going beyond the false dichotomy of same versus different processing, it is more important to understand how exactly faces are processed similarly or differently from objects. However, even by itself, face processing is poorly understood. Various aspects of face processing, such as holistic, configural, and face-space processing, are investigated in relative isolation, and the relationships between these are unclear. Furthermore, face processing is characteristically affected by various stimulus transformations such as inversion, contrast reversal and spatial frequency filtering, but how or why is unclear. Most importantly, we do not understand even the basic mechanisms of face processing. We hypothesize that what makes face processing distinctive is the existence of large, coarse face templates. We test our hypothesis by modifying an existing model of object processing to utilize such templates, and find that our model can account for many face-related phenomena. Using small, fine face templates as a control, we find that our model displays object-like processing characteristics instead. Overall, we believe that we may have made the first steps towards achieving a unified account of face processing. In addition, results from our control suggest that face and object processing share fundamental computational mechanisms. Coupled with recent advances in brain recording techniques, our results mean that face recognition could form the "tip of the spear" for attacking and solving the problem of visual recognition.by Cheston Y.-C. Tan.Ph.D

Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method

BACKGROUND: Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. However, there are still gaps toward whole-genome functional annotation of genes using the gene expression data. RESULTS: In this paper, we propose a novel technique called Fuzzy Nearest Clusters for genome-wide functional annotation of unclassified genes. The technique consists of two steps: an initial hierarchical clustering step to detect homogeneous co-expressed gene subgroups or clusters in each possibly heterogeneous functional class; followed by a classification step to predict the functional roles of the unclassified genes based on their corresponding similarities to the detected functional clusters. CONCLUSION: Our experimental results with yeast gene expression data showed that the proposed method can accurately predict the genes' functions, even those with multiple functional roles, and the prediction performance is most independent of the underlying heterogeneity of the complex functional classes, as compared to the other conventional gene function prediction approaches

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.[Display omitted]•A systematic inventory of HNSCC-associated proteins, phosphosites, and pathways•Three multi-omic subtypes linked to targeted treatment approaches and immunotherapy•Widespread deletion of immune modulatory genes accounts for loss of immunogenicity•Two modes of EGFR activation inform response to anti-EGFR monoclonal antibodiesHuang et al. report a proteogenomic study on 108 HPV-negative head and neck squamous cell carcinomas (HNSCCs). In addition to creating a comprehensive resource for pathogenic insights, multi-omic analysis identifies therapeutic hypotheses that may inform more precise approaches to treatment