Interpreting Deep Visual Representations via Network Dissection

The success of recent deep convolutional neural networks (CNNs) depends on learning hidden representations that can summarize the important factors of variation behind the data. However, CNNs often criticized as being black boxes that lack interpretability, since they have millions of unexplained model parameters. In this work, we describe Network Dissection, a method that interprets networks by providing labels for the units of their deep visual representations. The proposed method quantifies the interpretability of CNN representations by evaluating the alignment between individual hidden units and a set of visual semantic concepts. By identifying the best alignments, units are given human interpretable labels across a range of objects, parts, scenes, textures, materials, and colors. The method reveals that deep representations are more transparent and interpretable than expected: we find that representations are significantly more interpretable than they would be under a random equivalently powerful basis. We apply the method to interpret and compare the latent representations of various network architectures trained to solve different supervised and self-supervised training tasks. We then examine factors affecting the network interpretability such as the number of the training iterations, regularizations, different initializations, and the network depth and width. Finally we show that the interpreted units can be used to provide explicit explanations of a prediction given by a CNN for an image. Our results highlight that interpretability is an important property of deep neural networks that provides new insights into their hierarchical structure.Comment: *B. Zhou and D. Bau contributed equally to this work. 15 pages, 27 figure

Decolonizing Pathways towards Integrative Healing in Social Work

Taking a new and innovative angle on social work, this book seeks to remedy the lack of holistic perspectives currently used in Western social work practice by exploring Indigenous and other culturally diverse understandings and experiences of healing. This book examines six core areas of healing through a holistic lens that is grounded in a decolonizing perspective. Situating integrative healing within social work education and theory, the book takes an interdisciplinary approach, drawing from social memory and historical trauma, contemplative traditions, storytelling, healing literatures, integrative health, and the traditional environmental knowledge of Indigenous Peoples. In exploring issues of water, creative expression, movement, contemplation, animals, and the natural world in relation to social work practice, the book will appeal to all scholars, practitioners, and community members interested in decolonization and Indigenous studies

Volume 17, No. 3

Boyum, Steinar. “Philosophical Experience in Childhood.” 4­-12. Burdick, Stephanie. “Journaling Mendham.” 38-­40. Cassidy, Claire. “Children: Animals or Persons?” 13-­16. Guin, Phillip. “The Political and Social Ends of Philosophy.” 41­-46. Kohan, Walter. “Is it Possible to Think? A Response to Philip Guin.” 47-­50. Leeuw, Karel L. van der. “Philosophical Dialogue and the Search for Truth.” 17­-23. Matthews, Gareth B. “Thinking in Stories: Emily’s Art by Peter Catalanotto.” 1. Ming, Lam Chi. “Philosophy for Children in Hong Kong: A Pilot Study.” 24­-29. Shea, Peter. “Offering a Frame to put Experience In: Margaret Wise Brown Presents Ideas as Opportunities to Very Young Children.” 30-­37

2014 GREAT Day Program

SUNY Geneseo’s Eighth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1008/thumbnail.jp

Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications

abstract: Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading. To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time. Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists. Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Un/Dead Animal Art: Ethical Encounters Through Rogue Taxidermy Sculpture

Beginning in 2004, the Minnesota Association of Rogue Taxidermists began an art movement of taxidermied animal sculptures that challenged conventional forms of taxidermied objects massively produced and displayed on an international scale. In contrast to taxidermied ‘specimens’ found in museums, taxidermied ‘exotic’ wildlife decapitated and mounted on hunters\u27 walls, or synthetic taxidermied heads bought in department stores, rogue taxidermy artists create unconventional sculptures that are arguably antithetical to the ideologies shaped by previous generations: realism, colonialism, masculinity. As a pop-surrealist art movement chiefly practiced among women artists, rogue taxidermy artists follow an ethical mandate to never kill animals for the purposes of art and often display their sculptures in ways that are self-reflexive of speciesism and express criticisms of anthropocentrism. Through an intersectional feminist lens and alongside critical insights from (and debates within) postcolonialism, deconstruction, and affect theory, I analyze the art pieces created by Sarina Brewer, Angela Singer, Polly Morgan, Scott Bibus, and Robert Marbury. In doing so, I explore the ethical ambiguities of using postmortem animal bodies in an art movement that is informed by animal rights, and also discuss the complexity of animal-human relationships in the face of human conceptualized impressions of life and death. Brushing up against the history of public autopsies and other forms of body preservation, I look to the ways in which bodies are made ‘taxidermic’ through violence, trauma, objectification, commodification, bio-engineered artificiality, extinction, and the discriminatory practices that represented certain (animal and human) bodies as ‘unruly.’ Tackling the frames that produce ‘taxidermic’ bodies (as exposable and exploitable skins), I challenge the anthropocentrism foundational to human thought and highlight the ways that humans produce and perpetuate hollowed out crypts of meaning as it applies to animality. Essentially, this project attempts to undermine anthropocentric worldviews that construct humans as separate and unique from what is understood and described as the ‘nonhuman,’ and, also, invites readers to confront and acknowledge how vulnerability and mortality are shared among humans (animals) and other nonhuman beings

The identity construction and representation of diasporic Chinese content creators on YouTube

A significant number of diasporic Chinese content creators have emerged on YouTube in recent years. Unlike their parents, these Chinese diasporas in Western world spend most of their time in the receiving countries and have been marginalized by the mainstream society during their growing up period. With the intention to represent their own diasporic identity, a series of videos were made to share various cultural related content ranging from ethnic food preparation. generational relationships, and heritage language practices. Many of these videos have already received hundreds of thousands of views, showing its potential to have a large social influence. Thus, this study decided to examine how Chinese diaspora construct and represent their cultural identity on this platform, with a specific focus on the Chinese in Western countries. To understand the topic, this study will combine theories such as diaspora and transnationalism, cultural identity and semiotics, representation and power relations while also considering YouTube’s outstanding “participatory culture” and its commercial attributes. In terms of methodology, this study will treat YouTube’s environment as a whole and it has adopted a series of methods from online observation, semi-structured interview and textual analysis. The findings will be divided into three chapters with each chapter focusing on one cultural element (Chinese food, parents and heritage language) and the influence of these elements on Western Chinese identity construction and more importantly, how they represent these symbols online. During this process, power relations behind the representation process will be carefully investigated to understand how a hybrid identity was formulated through these online practices

Letters for a Newfoundland Dog and other encounters with nonhuman animals; Bird’s Work

This project encompasses a collection of lyric essays and a collection of poetry engaging with the topic of zoopoetics, which as a field is interested in the way that attentiveness to the poiesis of nonhuman animals can shape human creative forms. The lyric essays, which form my critical component, are each centered on what Donna Haraway would refer to as a ‘companion species,’ a term that extends beyond companion animals such as pets to include any animals we share our lives with. Looking at frogs, dogs, whales, cats, bats, and parrots, I explore my personal history with specific animals of these species, and also analyze their representation in literature, art, and popular culture. Within a zoopoetic framework, the essays engage with scholarship around anthropomorphism, animals and gender, animal captivity, and animal history. The poetry collection, which forms my creative component, explores various ways of writing nonhuman animals. Writing with curiosity and attentiveness towards non-human animals, I aim for my poems to embody the shared animal-human poiesis at the heart of zoopoetics