Search Results for author: Walter J. Scheirer

Found 32 papers, 6 papers with code

How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?

no code implementations27 Oct 2023 Zachariah Carmichael, Walter J. Scheirer

Surging interest in deep learning from high-stakes domains has precipitated concern over the inscrutable nature of black box neural networks.

Additive models Attribute +1

On the Effectiveness of Image Manipulation Detection in the Age of Social Media

no code implementations19 Apr 2023 Rosaura G. VidalMata, Priscila Saboia, Daniel Moreira, Grant Jensen, Jason Schlessman, Walter J. Scheirer

To this end, we introduce an anomaly enhancement loss that, when used with a residual architecture, improves the performance of different detection algorithms with a minimal introduction of false positives on the non-manipulated data.

Image Manipulation Image Manipulation Detection

Human Activity Recognition in an Open World

no code implementations23 Dec 2022 Derek S. Prijatelj, Samuel Grieggs, Jin Huang, Dawei Du, Ameya Shringi, Christopher Funk, Adam Kaufman, Eric Robertson, Walter J. Scheirer

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.

Human Activity Recognition

Using Human Perception to Regularize Transfer Learning

no code implementations15 Nov 2022 Justin Dulay, Walter J. Scheirer

Recent trends in the machine learning community show that models with fidelity toward human perceptual measurements perform strongly on vision tasks.

Transfer Learning

Psychophysical-Score: A Behavioral Measure for Assessing the Biological Plausibility of Visual Recognition Models

no code implementations16 Oct 2022 Brandon RichardWebster, Justin Dulay, Anthony DiFalco, Elisabetta Caldesi, Walter J. Scheirer

This article proposes a state-of-the-art procedure that generates a new metric, Psychophysical-Score, which is grounded in visual psychophysics and is capable of reliably estimating perceptual responses across numerous models -- representing a large range in complexity and biological inspiration.

Object Recognition

Guiding Machine Perception with Psychophysics

1 code implementation5 Jul 2022 Justin Dulay, Sonia Poltoratski, Till S. Hartmann, Samuel E. Anthony, Walter J. Scheirer

{G}{ustav} Fechner's 1860 delineation of psychophysics, the measurement of sensation in relation to its stimulus, is widely considered to be the advent of modern psychological science.

Forensic Analysis of Synthetically Generated Western Blot Images

no code implementations16 Dec 2021 Sara Mandelli, Davide Cozzolino, Edoardo D. Cannas, Joao P. Cardenuto, Daniel Moreira, Paolo Bestagini, Walter J. Scheirer, Anderson Rocha, Luisa Verdoliva, Stefano Tubaro, Edward J. Delp

As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images.

Binary Classification

A Study of the Human Perception of Synthetic Faces

no code implementations8 Nov 2021 Bingyu Shen, Brandon RichardWebster, Alice O'Toole, Kevin Bowyer, Walter J. Scheirer

In this paper, we introduce a study of the human perception of synthetic faces generated using different strategies including a state-of-the-art deep learning-based GAN model.

Face Generation

A Framework for Evaluating Post Hoc Feature-Additive Explainers

1 code implementation15 Jun 2021 Zachariah Carmichael, Walter J. Scheirer

In this work, we propose a framework for the evaluation of post hoc explainers on ground truth that is directly derived from the additive structure of a model.

Handwriting Recognition with Novelty

1 code implementation13 May 2021 Derek S. Prijatelj, Samuel Grieggs, Futoshi Yumoto, Eric Robertson, Walter J. Scheirer

This paper introduces an agent-centric approach to handle novelty in the visual recognition domain of handwriting recognition (HWR).

Handwriting Recognition

Pitfalls in Machine Learning Research: Reexamining the Development Cycle

no code implementations NeurIPS Workshop ICBINB 2020 Stella Biderman, Walter J. Scheirer

Machine learning has the potential to fuel further advances in data science, but it is greatly hindered by an ad hoc design process, poor data hygiene, and a lack of statistical rigor in model evaluation.

BIG-bench Machine Learning

Modeling Score Distributions and Continuous Covariates: A Bayesian Approach

no code implementations21 Sep 2020 Mel McCurrie, Hamish Nicholson, Walter J. Scheirer, Samuel Anthony

In biometric verification, model performance over continuous covariates---real-number attributes of images that affect performance---is particularly challenging to study.

Face Verification

A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks

1 code implementation20 Jun 2020 Derek S. Prijatelj, Mel McCurrie, Walter J. Scheirer

An interesting development in automatic visual recognition has been the emergence of tasks where it is not possible to assign objective labels to images, yet still feasible to collect annotations that reflect human judgements about them.

Age Estimation Attribute +3

UG$^{2+}$ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments

no code implementations9 Apr 2019 Ye Yuan, Wenhan Yang, Wenqi Ren, Jiaying Liu, Walter J. Scheirer, Zhangyang Wang

The UG$^{2+}$ challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.

Face Detection

Measuring Human Perception to Improve Handwritten Document Transcription

no code implementations7 Apr 2019 Samuel Grieggs, Bingyu Shen, Greta Rauch, Pei Li, Jiaqi Ma, David Chiang, Brian Price, Walter J. Scheirer

The subtleties of human perception, as measured by vision scientists through the use of psychophysics, are important clues to the internal workings of visual recognition.

On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs

no code implementations17 Nov 2018 Sandipan Banerjee, Walter J. Scheirer, Kevin W. Bowyer, Patrick J. Flynn

We propose a multi-scale GAN model to hallucinate realistic context (forehead, hair, neck, clothes) and background pixels automatically from a single input face mask.

Face Swapping Facial Inpainting +1

Fast Face Image Synthesis with Minimal Training

no code implementations5 Nov 2018 Sandipan Banerjee, Walter J. Scheirer, Kevin W. Bowyer, Patrick J. Flynn

Our method samples face components from a pool of multiple face images of real identities to generate the synthetic texture.

Image Generation

A Neurobiological Evaluation Metric for Neural Network Model Search

1 code implementation CVPR 2019 Nathaniel Blanchard, Jeffery Kinnison, Brandon RichardWebster, Pouya Bashivan, Walter J. Scheirer

In this paper we introduce a human-model similarity (HMS) metric, which quantifies the similarity of human fMRI and network activation behavior.

Face hallucination using cascaded super-resolution and identity priors

no code implementations28 May 2018 Klemen Grm, Simon Dobrišek, Walter J. Scheirer, Vitomir Štruc

In this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors.

Face Hallucination Face Recognition +2

Visual Psychophysics for Making Face Recognition Algorithms More Explainable

no code implementations ECCV 2018 Brandon RichardWebster, So Yon Kwon, Christopher Clarizio, Samuel E. Anthony, Walter J. Scheirer

Scientific fields that are interested in faces have developed their own sets of concepts and procedures for understanding how a target model system (be it a person or algorithm) perceives a face under varying conditions.

Face Recognition

Image Provenance Analysis at Scale

no code implementations19 Jan 2018 Daniel Moreira, Aparna Bharati, Joel Brogan, Allan Pinto, Michael Parowski, Kevin W. Bowyer, Patrick J. Flynn, Anderson Rocha, Walter J. Scheirer

Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images.

Authorship Verification Fact Checking

UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition

no code implementations9 Oct 2017 Rosaura G. Vidal, Sreya Banerjee, Klemen Grm, Vitomir Struc, Walter J. Scheirer

Advances in image restoration and enhancement techniques have led to discussion about how such algorithmscan be applied as a pre-processing step to improve automatic visual recognition.

Deblurring General Classification +2

Neuron Segmentation Using Deep Complete Bipartite Networks

no code implementations31 May 2017 Jianxu Chen, Sreya Banerjee, Abhinav Grama, Walter J. Scheirer, Danny Z. Chen

We propose a new FCN-type deep learning model, called deep complete bipartite networks (CB-Net), and a new scheme for leveraging approximate instance-wise annotation to train our pixel-wise prediction model.

Segmentation

SREFI: Synthesis of Realistic Example Face Images

no code implementations21 Apr 2017 Sandipan Banerjee, John S. Bernhard, Walter J. Scheirer, Kevin W. Bowyer, Patrick J. Flynn

In this paper, we propose a novel face synthesis approach that can generate an arbitrarily large number of synthetic images of both real and synthetic identities.

Face Generation Face Recognition

PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition

no code implementations19 Nov 2016 Brandon RichardWebster, Samuel E. Anthony, Walter J. Scheirer

By providing substantial amounts of data and standardized evaluation protocols, datasets in computer vision have helped fuel advances across all areas of visual recognition.

The Extreme Value Machine

no code implementations19 Jun 2015 Ethan M. Rudd, Lalit P. Jain, Walter J. Scheirer, Terrance E. Boult

It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time.

Incremental Learning

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