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Chapter and Conference Paper
Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport
While speech-enabled teachable agents have some advantages over ty**-based ones, they are vulnerable to errors stemming from misrecognition by automatic speech recognition (ASR). These errors may propagate, ...
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Chapter and Conference Paper
It Takes Two: Examining the Effects of Collaborative Teaching of a Robot Learner
Teaching others has been shown to be an activity in which students can learn new information in both human-human (peer-tutoring) and human-computer interactions (teachable robots). One factor that may help fos...
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Article
Predicting Visual Political Bias Using Webly Supervised Data and an Auxiliary Task
The news media shape public opinion, and often, the visual bias they contain is evident for careful human observers. This bias can be inferred from how different media sources portray different subjects or top...
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Chapter and Conference Paper
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection
Deep learning based object detectors are commonly deployed on mobile devices to solve a variety of tasks. For maximum accuracy, each detector is usually trained to solve one single specific task, and comes wit...
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Chapter and Conference Paper
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection
As mobile hardware technology advances, on-device computation is becoming more and more affordable.
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Chapter and Conference Paper
Domain Generalization Using Shape Representation
CNN-based representations have greatly advanced the state of the art in visual recognition, but the community has primarily focused on the setting where training and test set belong to the same dataset/distrib...
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Chapter and Conference Paper
Classifying Nuclei Shape Heterogeneity in Breast Tumors with Skeletons
In this study, we demonstrate the efficacy of scoring statistics derived from a medial axis transform, for differentiating tumor and non-tumor nuclei, in malignant breast tumor histopathology images. Character...
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Chapter and Conference Paper
Preserving Semantic Neighborhoods for Robust Cross-Modal Retrieval
The abundance of multimodal data (e.g. social media posts) has inspired interest in cross-modal retrieval methods. Popular approaches rely on a variety of metric learning losses, which prescribe what the proxi...
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Chapter and Conference Paper
Artistic Object Recognition by Unsupervised Style Adaptation
Computer vision systems currently lack the ability to reliably recognize artistically rendered objects, especially when such data is limited. In this paper, we propose a method for recognizing objects in artis...
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Chapter and Conference Paper
ADVISE: Symbolism and External Knowledge for Decoding Advertisements
In order to convey the most content in their limited space, advertisements embed references to outside knowledge via symbolism. For example, a motorcycle stands for adventure (a positive property the ad wants ...
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Chapter
Attributes for Image Retrieval
Image retrieval is a computer vision application that people encounter in their everyday lives. To enable accurate retrieval results, a human user needs to be able to communicate in a rich and noiseless way wi...
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Article
WhittleSearch: Interactive Image Search with Relative Attribute Feedback
We propose a novel mode of feedback for image search, where a user describes which properties of exemplar images should be adjusted in order to more closely match his/her mental model of the image sought. For ...
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Article
Discovering Attribute Shades of Meaning with the Crowd
To learn semantic attributes, existing methods typically train one discriminative model for each word in a vocabulary of nameable properties. However, this “one model per word” assumption is problematic: while...
