Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work

Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the development of computer vision. They can not only focus on target parts to learn discriminative local representations, but also process information selectively to improve the efficiency. In terms of application scenarios and paradigms, local mechanisms have different characteristics. In this survey, we provide a systematic review of local mechanisms for various computer vision tasks and approaches, including fine-grained visual recognition, person re-identification, few-/zero-shot learning, multi-modal learning, self-supervised learning, Vision Transformers, and so on. Categorization of local mechanisms in each field is summarized. Then, advantages and disadvantages for every category are analyzed deeply, leaving room for exploration. Finally, future research directions about local mechanisms have also been discussed that may benefit future works. To the best our knowledge, this is the first survey about local mechanisms on computer vision. We hope that this survey can shed light on future research in the computer vision field

Semi-Supervised and Unsupervised Deep Visual Learning: A Survey

State-of-the-art deep learning models are often trained with a large amountof costly labeled training data. However, requiring exhaustive manualannotations may degrade the model's generalizability in the limited-labelregime. Semi-supervised learning and unsupervised learning offer promisingparadigms to learn from an abundance of unlabeled visual data. Recent progressin these paradigms has indicated the strong benefits of leveraging unlabeleddata to improve model generalization and provide better model initialization.In this survey, we review the recent advanced deep learning algorithms onsemi-supervised learning (SSL) and unsupervised learning (UL) for visualrecognition from a unified perspective. To offer a holistic understanding ofthe state-of-the-art in these areas, we propose a unified taxonomy. Wecategorize existing representative SSL and UL with comprehensive and insightfulanalysis to highlight their design rationales in different learning scenariosand applications in different computer vision tasks. Lastly, we discuss theemerging trends and open challenges in SSL and UL to shed light on futurecritical research directions.<br

Advancing Statistical Inference For Population Studies In Neuroimaging Using Machine Learning

Modern neuroimaging techniques allow us to investigate the brain in vivo and in high resolution, providing us with high dimensional information regarding the structure and the function of the brain in health and disease. Statistical analysis techniques transform this rich imaging information into accessible and interpretable knowledge that can be used for investigative as well as diagnostic and prognostic purposes. A prevalent area of research in neuroimaging is group comparison, i.e., the comparison of the imaging data of two groups (e.g. patients vs. healthy controls or people who respond to treatment vs. people who don\u27t) to identify discriminative imaging patterns that characterize different conditions. In recent years, the neuroimaging community has adopted techniques from mathematics, statistics, and machine learning to introduce novel methodologies targeting the improvement of our understanding of various neuropsychiatric and neurodegenerative disorders. However, existing statistical methods are limited by their reliance on ad-hoc assumptions regarding the homogeneity of disease effect, spatial properties of the underlying signal and the covariate structure of data, which imposes certain constraints about the sampling of datasets. 1. First, the overarching assumption behind most analytical tools, which are commonly used in neuroimaging studies, is that there is a single disease effect that differentiates the patients from controls. In reality, however, the disease effect may be heterogeneously expressed across the patient population. As a consequence, when searching for a single imaging pattern that characterizes the difference between healthy controls and patients, we may only get a partial or incomplete picture of the disease effect. 2. Second, and importantly, most analyses assume a uniform shape and size of disease effect. As a consequence, a common step in most neuroimaging analyses it to apply uniform smoothing of the data to aggregate regional information to each voxel to improve the signal to noise ratio. However, the shape and size of the disease patterns may not be uniformly represented across the brain. 3. Lastly, in practical scenarios, imaging datasets commonly include variations due to multiple covariates, which often have effects that overlap with the searched disease effects. To minimize the covariate effects, studies are carefully designed by appropriately matching the populations under observation. The difficulty of this task is further exacerbated by the advent of big data analyses that often entail the aggregation of large datasets collected across many clinical sites. The goal of this thesis is to address each of the aforementioned assumptions and limitations by introducing robust mathematical formulations, which are founded on multivariate machine learning techniques that integrate discriminative and generative approaches. Specifically, 1. First, we introduce an algorithm termed HYDRA which stands for heterogeneity through discriminative analysis. This method parses the heterogeneity in neuroimaging studies by simultaneously performing clustering and classification by use of piecewise linear decision boundaries. 2. Second, we propose to perform regionally linear multivariate discriminative statistical mapping (MIDAS) toward finding the optimal level of variable smoothing across the brain anatomy and tease out group differences in neuroimaging datasets. This method makes use of overlapping regional discriminative filters to approximate a matched filter that best delineates the underlying disease effect. 3. Lastly, we develop a method termed generative discriminative machines (GDM) toward reducing the effect of confounds in biased samples. The proposed method solves for a discriminative model that can also optimally generate the data when taking into account the covariate structure. We extensively validated the performance of the developed frameworks in the presence of diverse types of simulated scenarios. Furthermore, we applied our methods on a large number of clinical datasets that included structural and functional neuroimaging data as well as genetic data. Specifically, HYDRA was used for identifying distinct subtypes of Alzheimer\u27s Disease. MIDAS was applied for identifying the optimally discriminative patterns that differentiated between truth-telling and lying functional tasks. GDM was applied on a multi-site prediction setting with severely confounded samples. Our promising results demonstrate the potential of our methods to advance neuroimaging analysis beyond the set of assumptions that limit its capacity and improve statistical power