Jump to Content
Andrew Gallagher

Andrew Gallagher

I joined Google in 2014. Previously, I was a Visiting Research Scientist at Cornell University's School of Electrical and Computer Engineering, beginning in June 2012. I earned the Ph.D. degree in electrical and computer engineering from Carnegie Mellon University in 2009, advised by Prof. Tsuhan Chen. I received an M.S. degree from Rochester Institute of Technology, and the B.S. degree from Geneva College, both in electrical engineering. I worked for the Eastman Kodak Company for over a decade during the fascinating transition from chemical to digital imaging, initially developing image enhancement algorithms for digital photofinishing. These algorithms were shipped under the trade name "Kodak Perfect Touch" in photo printing mini-labs, and millions of digital cameras, and enhanced many billions of images. I enjoy working on tough and interesting problems.
Authored Publications
Google Publications
Other Publications
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    Preview abstract We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs. We propose a novel one-class open-set OOD detector that leverages text-image pre-trained models in a zero-shot fashion and incorporates various descriptions of in-domain and OOD. Our approach is designed to detect anything not in-domain and offers the flexibility to detect a wide variety of OOD, defined via fine- or coarse-grained labels, or even in natural language. We evaluate our approach on challenging benchmarks including large-scale datasets containing fine-grained, semantically similar classes, distributionally shifted images, and multi-object images containing a mixture of in-domain and OOD objects. Our method shows superior performance over previous methods on all benchmarks. View details
    Preview abstract Perhaps surprisingly, recent studies have shown probabilistic model likelihoods have poor specificity for out-of-distribution (OOD) detection and often assign higher likelihoods to OOD data than in-distribution data. To ameliorate this issue we propose DoSE, the density of states estimator. Drawing on the statistical physics notion of ``density of states,'' the DoSE decision rule avoids direct comparison of model probabilities, and instead utilizes the ``probability of the model probability,'' or indeed the frequency of any reasonable statistic. The frequency is calculated using nonparametric density estimators (e.g., KDE and one-class SVM) which measure the typicality of various model statistics given the training data and from which we can flag test points with low typicality as anomalous. Unlike many other methods, DoSE requires neither labeled data nor OOD examples. DoSE is modular and can be trivially applied to any existing, trained model. We demonstrate DoSE's state-of-the-art performance against other unsupervised OOD detectors on previously established ``hard'' benchmarks. View details
    von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning
    Tyler Scott
    Michael Mozer
    International Conference on Computer Vision 2021 (to appear)
    Preview abstract Recent work has argued that classification losses utilizing softmax cross-entropy are superior not only for fixed-set classification tasks, but they outperform losses developed specifically for open-set tasks including few-shot learning and retrieval. Softmax classifiers have been studied using different embedding geometries--Euclidean, hyperbolic, and spherical--and claims have been made about the superiority of one or another, but they have not been systematically compared with careful controls. We conduct an empirical investigation of embedding geometry on softmax losses for a variety of fixed-set classification and image retrieval tasks. Interesting properties observed for the spherical methods lead us to propose a probabilistic classifier based on the von Mises-Fisher distribution, and we show that it is competitive with state-of-the-art methods while producing improved out-of-the-box calibration. We provide guidance regarding the trade-offs between methods and how to choose among them. View details
    Preview abstract Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning. Generally approximate posteriors learned by ADVI are forced to be unimodal in order to facilitate use of the reparameterization trick. In this paper, we show how stratified sampling may be used to enable mixture distributions as the approximate posterior, and derive a new lower bound on the evidence analogous to the importance weighted autoencoder (IWAE). We show that this "SIWAE" is a tighter bound than both IWAE and the traditional ELBO, both of which are special instances of this bound. We verify empirically that the traditional ELBO objective disfavors the presence of multimodal posterior distributions and may therefore not be able to fully capture structure in the latent space. Our experiments show that using the SIWAE objective allows the encoder to learn more complex distributions which regularly contain multimodality, resulting in higher accuracy and better calibration in the presence of incomplete, limited, or corrupted data. View details
    Preview abstract Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual active speaker dataset has limited evaluation in terms of data diversity, environments, and accuracy. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) which has been publicly released to facilitate algorithm development and comparison. It contains temporally labeled face tracks in videos, where each face instance is labeled as speaking or not, and whether the speech is audible. The dataset contains about 3.65 million human labeled frames spanning 38.5 hours. We also introduce a state-of-the-art, jointly trained audio-visual model for real-time active speaker detection and compare several variants. The evaluation clearly demonstrates a significant gain due to audio-visual modeling and temporal integration over multiple frames. View details
    Preview abstract Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a single point in the embedding space. Often the distance between points is used as a proxy for match confidence. However, this can fail to represent uncertainty which can arise when the input is ambiguous, e.g., due to occlusion or blurriness. This work addresses this issue and explicitly models the uncertainty by “hedging” the location of each input in the embedding space. We introduce the hedged instance embedding (HIB) in which embeddings are modeled as random variables and the model is trained under the variational information bottleneck principle (Alemi et al., 2016; Achille & Soatto, 2018). Empirical results on our new N-digit MNIST dataset show that our method leads to the desired behavior of “hedging its bets” across the embedding space upon encountering ambiguous inputs. This results in improved performance for image matching and classification tasks, more structure in the learned embedding space, and an ability to compute a per-exemplar uncertainty measure which is correlated with downstream performance. View details
    Preview abstract Speech activity detection (or endpointing) is an important processing step for applications such as speech recognition, language identification and speaker diarization. Both audio- and vision-based approaches have been used for this task in various settings and with multiple variations tailored toward applications. Unfortunately, much of the prior work reports results in synthetic settings, on task-specific datasets, or on datasets that are not openly available. This makes it difficult to compare approaches in similar settings and to understand their strengths and weaknesses. In this paper, we describe a new dataset of densely labeled speech activity in YouTube video clips, which has been designed to address these issues and will be released publicly. The dataset labels go beyond speech alone, annotating three specific speech activity situations: clean speech, speech and music co-occurring, and speech and noise co-occurring. These classes will enable further analysis of model performance in the presence of noise. We report benchmark performance numbers on this dataset using state-of-the-art audio and vision models. View details
    Preview abstract Face images are one of the main areas of focus for computer vision, receiving on a wide variety of tasks. Although face recognition is probably the most widely researched, many other tasks such as kinship detection, facial expression classification and facial aging have been examined. In this work we propose the new task of face similarity. That is, we predict the perceived similarity between facial images, given that they are \textit{not} of the same person. Although this task is clearly correlated with face recognition, it is different and therefore justifies a separate investigation. Humans often remark that two persons look alike, even in cases where the persons are not actually confused with one another. In addition, because face similarity is different than traditional image similarity, there are challenges in data collection and labeling, and dealing with diverging opinions between human labelers. We present evidence that finding facial look-alikes and recognizing faces are two distinct tasks. We propose a new dataset for facial similarity and introduce the Lookalike network that is directed towards similar face classification and outperforms the ad hoc usage of usage of a face recognition network directed at the same task. View details
    VIP: Finding Important People in Images
    Clint Solomon Mathialagan
    Dhruv Batra
    Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition (2015), pp. 4858-4966
    Preview abstract People preserve memories of events such as birthdays, weddings, or vacations by capturing photos, often depicting groups of people. Invariably, some individuals in the image are more important than others given the context of the event. This paper analyzes the concept of the importance of individuals in group photographs. We address two specific questions – Given an image, who are the most important individuals in it? Given multiple images of a person, which image depicts the person in the most important role? We introduce a measure of importance of people in images and investigate the correlation between importance and visual saliency. We find that not only can we automatically predict the importance of people from purely visual cues, incorporating this predicted importance results in signifi- cant improvement in applications such as im2text (generating sentences that describe images of groups of people). View details
    A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions
    Kuan-Chuan Peng
    Amir Sadovnik
    Tsuhan Chen
    Computer Vision Pattern Recognition (CVPR), Computer Vision Pattern Recognition (CVPR), Computer Vision Pattern Recognition (CVPR) (2015)