no code implementations • 19 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.
no code implementations • 19 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.
no code implementations • 21 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.
no code implementations • 31 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.
no code implementations • 9 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.
no code implementations • 19 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.
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.
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.
no code implementations • 28 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.
no code implementations • WS 2018 • Nathaniel Blanchard, Daniel Moreira, Aparna Bharati, Walter J. Scheirer
We discard traditional transcription features in order to minimize human intervention and to maximize the deployability of our model on at-scale real-world data.
no code implementations • 9 Jul 2018 • Aparna Bharati, Daniel Moreira, Joel Brogan, Patricia Hale, Kevin W. Bowyer, Patrick J. Flynn, Anderson Rocha, Walter J. Scheirer
Creative works, whether paintings or memes, follow unique journeys that result in their final form.
no code implementations • 5 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.
no code implementations • 17 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.
no code implementations • 28 Jan 2019 • Rosaura G. VidalMata, Sreya Banerjee, Brandon RichardWebster, Michael Albright, Pedro Davalos, Scott McCloskey, Ben Miller, Asong Tambo, Sushobhan Ghosh, Sudarshan Nagesh, Ye Yuan, Yueyu Hu, Junru Wu, Wenhan Yang, Xiaoshuai Zhang, Jiaying Liu, Zhangyang Wang, Hwann-Tzong Chen, Tzu-Wei Huang, Wen-Chi Chin, Yi-Chun Li, Mahmoud Lababidi, Charles Otto, Walter J. Scheirer
From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area.
no code implementations • 7 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.
no code implementations • 9 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.
no code implementations • 26 Jul 2019 • Sreya Banerjee, Rosaura G. VidalMata, Zhangyang Wang, Walter J. Scheirer
How can we effectively engineer a computer vision system that is able to interpret videos from unconstrained mobility platforms like UAVs?
no code implementations • 29 Jan 2020 • Rosaura G. VidalMata, Walter J. Scheirer, Anna Kukleva, David Cox, Hilde Kuehne
Understanding the structure of complex activities in untrimmed videos is a challenging task in the area of action recognition.
1 code implementation • 20 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.
no code implementations • 21 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.
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.
1 code implementation • 13 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).
1 code implementation • 15 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.
no code implementations • 8 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.
no code implementations • 16 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.
1 code implementation • 5 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.
no code implementations • 16 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.
no code implementations • 15 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.
no code implementations • 23 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.
no code implementations • 19 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.
1 code implementation • 7 Aug 2023 • Sophia J. Abraham, Kehelwala D. G. Maduranga, Jeffery Kinnison, Zachariah Carmichael, Jonathan D. Hauenstein, Walter J. Scheirer
Traditional methods, like grid search and Bayesian optimization, often struggle to quickly adapt and efficiently search the loss landscape.
no code implementations • 27 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.