Multiclass Alignment of Confidence and Certainty for Network Calibration

Deep neural networks (DNNs) have made great strides in pushing the state-of-the-art in several challenging domains. Recent studies reveal that they are prone to making overconfident predictions. This greatly reduces the overall trust in model predictions, especially in safety-critical applications. Early work in improving model calibration employs post-processing techniques which rely on limited parameters and require a hold-out set. Some recent train-time calibration methods, which involve all model parameters, can outperform the postprocessing methods. To this end, we propose a new train-time calibration method, which features a simple, plug-and-play auxiliary loss known as multi-class alignment of predictive mean confidence and predictive certainty (MACC). It is based on the observation that a model miscalibration is directly related to its predictive certainty, so a higher gap between the mean confidence and certainty amounts to a poor calibration both for in-distribution and out-of-distribution predictions. Armed with this insight, our proposed loss explicitly encourages a confident (or underconfident) model to also provide a low (or high) spread in the presoftmax distribution. Extensive experiments on ten challenging datasets, covering in-domain, out-domain, non-visual recognition and medical image classification scenarios, show that our method achieves state-of-the-art calibration performance for both in-domain and out-domain predictions. Our code and models will be publicly released.Comment: Accepted at GCPR 202

Synergy between face alignment and tracking via Discriminative Global Consensus Optimization

An open question in facial landmark localization in video is whether one should perform tracking or tracking-by-detection (i.e. face alignment). Tracking produces fittings of high accuracy but is prone to drifting. Tracking-by-detection is drift-free but results in low accuracy fittings. To provide a solution to this problem, we describe the very first, to the best of our knowledge, synergistic approach between detection (face alignment) and tracking which completely eliminates drifting from face tracking, and does not merely perform tracking-by-detection. Our first main contribution is to show that one can achieve this synergy between detection and tracking using a principled optimization framework based on the theory of Global Variable Consensus Optimization using ADMM; Our second contribution is to show how the proposed analytic framework can be integrated within state-of-the-art discriminative methods for face alignment and tracking based on cascaded regression and deeply learned features. Overall, we call our method Discriminative Global Consensus Model (DGCM). Our third contribution is to show that DGCM achieves large performance improvement over the currently best performing face tracking methods on the most challenging category of the 300-VW dataset

Aga Khan Museum and Ismaili Centre as alternative planning model for mosque development

Multiculturalism is widely celebrated in Toronto as a cornerstone of our society. When multiculturalism moves outside festivals and food, groups make spatial claims of citizenship and identity, the experience is somewhat different. There is no doubt that some racialized minorities have fared well in the Greater Toronto Area. Their growth is no longer confined to low-income enclaves within the City of Toronto but into city suburbs. This growth comes with the increased demand for spatial citizenship through culturally suited social, recreational, commercial and religious space. It is here where the experience of multiculturalism changes. The inherently political and contentious process of land use planning and its response to individual groups needs for certain type of developments is the broad focus of this paper. The paper looks at how the practice of planning in the Greater Toronto Area has responded to social diversity in cities by studying the specific process of mosque development for Muslim Canadians. Mosque development has faced challenges in the planning arena through staunch opposition that often hides behind legitimate planning technicalities to express the personal distaste for a group of people. My goal was to understand the role of planning departments in recognizing and responding to the rise of these conflicts in land use development. he paper examines the development process of five specific traditional mosques in the Toronto area to identify disputes and challenges. These are compared with a different type of Islamic development--the Aga Khan Museum, Park and Ismaili Centre--to better understand how features such as multifunctionality, scale and status appear more acceptable to planning and the general public producing fewer obstacles in its development as compared to traditional mosque development. I look at how wealth, starchitecture, the framing of the development as cultural rather than a religious, and the support of local organizations contribute to the success and acceptance of a project, as compared to traditional mosque developments. The paper is organized into three sections: 1) a review of the Aga Khan development in order to understand the purpose and the development process; 2) an examination of the development of more conventional mosques in the Greater Toronto Area with an emphasis on the challenges in such developments; and 3) an analysis elucidating some material concepts and themes that emerge from the case studies in order to facilitate in improving the planning process for mosques

Generalizing to Unseen Domains in Diabetic Retinopathy Classification

Diabetic retinopathy (DR) is caused by long-standing diabetes and is among the fifth leading cause for visual impairments. The process of early diagnosis and treatments could be helpful in curing the disease, however, the detection procedure is rather challenging and mostly tedious. Therefore, automated diabetic retinopathy classification using deep learning techniques has gained interest in the medical imaging community. Akin to several other real-world applications of deep learning, the typical assumption of i.i.d data is also violated in DR classification that relies on deep learning. Therefore, developing DR classification methods robust to unseen distributions is of great value. In this paper, we study the problem of generalizing a model to unseen distributions or domains (a.k.a domain generalization) in DR classification. To this end, we propose a simple and effective domain generalization (DG) approach that achieves self-distillation in vision transformers (ViT) via a novel prediction softening mechanism. This prediction softening is an adaptive convex combination one-hot labels with the model's own knowledge. We perform extensive experiments on challenging open-source DR classification datasets under both multi-source and single-source DG settings with three different ViT backbones to establish the efficacy and applicability of our approach against competing methods. For the first time, we report the performance of several state-of-the-art DG methods on open-source DR classification datasets after conducting thorough experiments. Finally, our method is also capable of delivering improved calibration performance than other methods, showing its suitability for safety-critical applications, including healthcare. We hope that our contributions would investigate more DG research across the medical imaging community.Comment: Accepted at WACV 202

Visual tracking over multiple temporal scales

Visual tracking is the task of repeatedly inferring the state (position, motion, etc.) of the desired target in an image sequence. It is an important scientific problem as humans can visually track targets in a broad range of settings. However, visual tracking algorithms struggle to robustly follow a target in unconstrained scenarios. Among the many challenges faced by visual trackers, two important ones are occlusions and abrupt motion variations. Occlusions take place when (an)other object(s) obscures the camera's view of the tracked target. A target may exhibit abrupt variations in apparent motion due to its own unexpected movement, camera movement, and low frame rate image acquisition. Each of these issues can cause a tracker to lose its target. This thesis introduces the idea of learning and propagation of tracking information over multiple temporal scales to overcome occlusions and abrupt motion variations. A temporal scale is a specific sequence of moments in time Models (describing appearance and/or motion of the target) can be learned from the target tracking history over multiple temporal scales and applied over multiple temporal scales in the future. With the rise of multiple motion model tracking frameworks, there is a need for a broad range of search methods and ways of selecting between the available motion models. The potential benefits of learning over multiple temporal scales are first assessed by studying both motion and appearance variations in the ground-truth data associated with several image sequences. A visual tracker operating over multiple temporal scales is then proposed that is capable of handling occlusions and abrupt motion variations. Experiments are performed to compare the performance of the tracker with competing methods, and to analyze the impact on performance of various elements of the proposed approach. Results reveal a simple, yet general framework for dealing with occlusions and abrupt motion variations. In refining the proposed framework, a search method is generalized for multiple competing hypotheses in visual tracking, and a new motion model selection criterion is proposed

Short-Term Load Forecasting Using AMI Data

Accurate short-term load forecasting is essential for efficient operation of the power sector. Predicting load at a fine granularity such as individual households or buildings is challenging due to higher volatility and uncertainty in the load. In aggregate loads such as at grids level, the inherent stochasticity and fluctuations are averaged-out, the problem becomes substantially easier. We propose an approach for short-term load forecasting at individual consumers (households) level, called Forecasting using Matrix Factorization (FMF). FMF does not use any consumers' demographic or activity patterns information. Therefore, it can be applied to any locality with the readily available smart meters and weather data. We perform extensive experiments on three benchmark datasets and demonstrate that FMF significantly outperforms the computationally expensive state-of-the-art methods for this problem. We achieve up to 26.5% and 24.4 % improvement in RMSE over Regression Tree and Support Vector Machine, respectively and up to 36% and 73.2% improvement in MAPE over Random Forest and Long Short-Term Memory neural network, respectively

Granulomatosis with polyangiitis: A 17 year experience from a tertiary care hospital in Pakistan

Objective: Granulomatosis with Polyangiitis (GPA) is an autoimmune, multi-system, small and medium vessel vasculitis with granulomatous inflammation. Aim of this study was to assess the clinical and radiological presentations of patients with GPA amongst the Pakistani population. It is a single centre retrospective single observation study. Results: Study was conducted at the Aga Khan University Hospital, Karachi with records were reviewed from January 2000 to December 2017. Definitive diagnosis was made using a combination of serological anti-neutrophil cytoplasmic antibody (ANCA) testing along with the clinical and radiological presentation. A total of 51 patients met the diagnostic criteria in the time frame of the study. There were 23 males and 28 females with mean age of 44.0 ± 17.8 years on presentation. Arthritis was the most common symptom present in 41.2% of the cases followed by cough in 32.0%. Sixteen patients showed pulmonary infiltrates on chest X-ray. C-ANCA was positive in all of the patients compared with 21.6% p-ANCA positivity. A total of 13 biopsies were done. The median Birmingham Vasculitis Activity Score was 12. We report a 17.6% mortality rate with 5 deaths occurring due to respiratory failure. GPA is a diagnostic challenge leading to late diagnosis which can contribute to significant morbidity and mortality specially in the Third World