Author name cluster

Walter J. Scheirer

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

JAIR Journal 2024 Journal Article

Human Activity Recognition in an Open World

Derek S. Prijatelj
Samuel Grieggs
Jin Huang
Dawei Du
Ameya Shringi
Christopher Funk
Adam Kaufman
Eric Robertson

Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other ways. Novelty may be task-relevant, such as a new class or new features, or task-irrelevant resulting in nuisance novelty, such as never before seen noise, blur, or distorted video recordings. To perform HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and learn over time in the face of novelty. This paper 1) formalizes the definition of novelty in HAR building upon the prior definition of novelty in classification tasks, 2) proposes an incremental open world learning (OWL) protocol and applies it to the Kinetics datasets to generate a new benchmark KOWL-718, 3) analyzes the performance of current stateof-the-art HAR models when novelty is introduced over time, 4) provides a containerized and packaged pipeline for reproducing the OWL protocol and for modifying for any future updates to Kinetics. The experimental analysis includes an ablation study of how the different models perform under various conditions as annotated by Kinetics-AVA. The code may be used to analyze different annotations and subsets of the Kinetics datasets in an incremental open world fashion, as well as be extended as further updates to Kinetics are released.

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AAAI Conference 2023 Conference Paper

Unfooling Perturbation-Based Post Hoc Explainers

Zachariah Carmichael
Walter J. Scheirer

Monumental advancements in artificial intelligence (AI) have lured the interest of doctors, lenders, judges, and other professionals. While these high-stakes decision-makers are optimistic about the technology, those familiar with AI systems are wary about the lack of transparency of its decision-making processes. Perturbation-based post hoc explainers offer a model agnostic means of interpreting these systems while only requiring query-level access. However, recent work demonstrates that these explainers can be fooled adversarially. This discovery has adverse implications for auditors, regulators, and other sentinels. With this in mind, several natural questions arise - how can we audit these black box systems? And how can we ascertain that the auditee is complying with the audit in good faith? In this work, we rigorously formalize this problem and devise a defense against adversarial attacks on perturbation-based explainers. We propose algorithms for the detection (CAD-Detect) and defense (CAD-Defend) of these attacks, which are aided by our novel conditional anomaly detection approach, KNN-CAD. We demonstrate that our approach successfully detects whether a black box system adversarially conceals its decision-making process and mitigates the adversarial attack on real-world data for the prevalent explainers, LIME and SHAP. The code for this work is available at https://github.com/craymichael/unfooling.

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IS Journal 2018 Journal Article

Trends and Controversies

Hugo Proenca
Mark Nixon
Michele Nappi
Esam Ghaleb
Gokhan Ozbulak
Hua Gao
Hazim Kemal Ekenel
Klemen Grm

Performing covert biometric recognition in surveillance environments has been regarded as a grand challenge, considering the adversity of the conditions where recognition should be carried out (e. g. , poor resolution, bad lighting, off-pose and partially occluded data). This special issue compiles a group of approaches to this problem.

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IROS Conference 2017 Conference Paper

Déjà vu: Scalable place recognition using mutually supportive feature frequencies

Adam Jacobson
Walter J. Scheirer
Michael Milford

Learning and recognition is a fundamental process performed in many robot operations such as mapping and localization. The majority of approaches share some common characteristics, such as attempting to extract salient features, landmarks or signatures, and growth in data storage and computational requirements as the size of the environment increases. In biological systems, spatial encoding in the brain is definitively known to be performed using a fixed-size neural encoding framework - the place, head-direction and grid cells found in the mammalian hippocampus and entorhinal cortex. Particularly paradoxically, one of the main encoding centers - the grid cells - represents the world using a highly aliased, repetitive encoding structure where one neuron represents an unbounded number of places in the world. Inspired by this system, in this paper we invert the normal approach used in forming mapping and localization algorithms, by developing a novel place recognition algorithm that seeks out and leverages repetitive, mutually complementary landmark frequencies in the world. The combinatorial encoding capacity of multiple different frequencies enables not only the ability to achieve efficient data storage, but also the potential for sub-linear storage growth in a learning and recall system. Using both ground-based and aerial camera datasets, we demonstrate the system finding and utilizing these frequencies to achieve successful place recognition, and discuss how this approach might scale to arbitrarily large global datasets and dimensions.

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ICRA Conference 2014 Conference Paper

Condition-invariant, top-down visual place recognition

Michael Milford
Walter J. Scheirer
Eleonora Vig
Arren Glover
Oliver Baumann
Jason B. Mattingley
David D. Cox

In this paper we present a novel, condition-invariant place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images, alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We conduct an exhaustive set of experiments evaluating the relationship between place recognition performance and computational resources using part of the challenging Alderley sunny day — rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. We achieve recall rates of up to 51% at 100% precision, matching places that have undergone drastic perceptual change while rejecting match hypotheses between highly aliased images of different places. Human trials demonstrate the performance is approaching human capability. The results provide a new benchmark for single image, condition-invariant place recognition.

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