UAC: An Uncertainty-Aware Face Clustering Algorithm

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We investigate ways to leverage uncertainty in face images to improve the quality of the face clusters. We observe that popular clustering algorithms do not produce better quality clusters when clustering probabilistic face represen- tations that implicitly model uncertainty – these algorithms predict up to 9.6X more clusters than the ground truth for the IJB-A benchmark. We empirically analyze the causes for this unexpected behavior and identify excessive false- positives and false-negatives (when comparing face-pairs) as the main reasons for poor quality clustering. Based on this insight, we propose an uncertainty-aware clustering al- gorithm, UAC, which explicitly leverages uncertainty infor- mation during clustering to decide when a pair of faces are similar or when a predicted cluster should be discarded. UAC considers (a) uncertainty of faces in face-pairs, (b) bins face-pairs into different categories based on an un- certainty threshold, (c) intelligently varies the similarity threshold during clustering to reduce false-negatives and false-positives, and (d) discards predicted clusters that ex- hibit a high measure of uncertainty. Extensive experimen- tal results on several popular benchmarks and comparisons with state-of-the-art clustering methods show that UAC pro- duces significantly better clusters by leveraging uncertainty in face images – predicted number of clusters is up to 0.18X more of the ground truth for the IJB-A benchmark.

F3S: Free Flow Fever Screening

In 4th International Workshop on EdgeDL: Deep Learning on Edge for Smart Health and Wellbeing Applications (EdgeDL 2021) - Co-located with the IEEE International Conference on Smart Computing (SMARTCOMP 2021) Identification of people with elevated body temperature can reduce or dramatically slow down the spread of infectious diseases like COVID-19. We present a novel fever-screening … Continue reading F3S: Free Flow Fever Screening

ECO: Edge-Cloud Optimization of 5G applications

In The 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021), Melbourne, Victoria, Australia Centralized cloud computing with 100+ milliseconds network latencies cannot meet the tens of milliseconds to sub- millisecond response times required for emerging 5G applications like autonomous driving, smart manufacturing, tactile internet, and augmented or virtual reality. We describe … Continue reading ECO: Edge-Cloud Optimization of 5G applications

First step of my journey for rediscovering the straight path (aka I’m using again GNU Emacs after many years)

"... mi ritrovai per una selva oscura, ché la diritta via era smarrita ..." (Divina Commedia - Canto I) Like Dante in his allegoric journey, I (also) "found myself deep in a darkened forest, for I had lost all trace of the straight path" in the computing world. The "darkened forest" in which I found … Continue reading First step of my journey for rediscovering the straight path (aka I’m using again GNU Emacs after many years)

A Coprocessor Sharing-Aware Scheduler for Xeon Phi-Based Compute Clusters

In Parallel and Distributed Processing Symposium, 2014 IEEE 28th International We propose a cluster scheduling technique for compute clusters with Xeon Phi coprocessors. Even though the Xeon Phi runs Linux which allows multiprocessing, cluster schedulers generally do not allow jobs to share coprocessors because sharing can cause oversubscription of coprocessor memory and thread resources. It … Continue reading A Coprocessor Sharing-Aware Scheduler for Xeon Phi-Based Compute Clusters