Search Results for author:
Found 30 papers, 10 papers with code
A graph-transformer for whole slide image classification
Here we present a Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images, called GTP, to predict disease grade.
SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset
In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1, 400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism.
A Unified Framework for Domain Adaptive Pose Estimation
In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision.
Exploring Consistency in Cross-Domain Transformer for Domain Adaptive Semantic Segmentation
We propose to perform adaptation on attention maps with cross-domain attention layers that share features between the source and the target domains.
CDAC: Cross-domain Attention Consistency in Transformer for Domain Adaptive Semantic Segmentation
While transformers have greatly boosted performance in semantic segmentation, domain adaptive transformers are not yet well explored.
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes
While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly.
Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types
We also propose a scrape-by-keywords methodology and used it to scrape ~30, 000 images and their captions of 38 Kenyan food types.
OpenFraming: We brought the ML; you bring the data. Interact with your data and discover its frames
The degree of user involvement is flexible: they can run models that have been pre-trained on select issues; submit labeled documents and train a new model for frame classification; or submit unlabeled documents and obtain potential frames of the documents.
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data
We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research.
Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)
We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems.
Dynamic Allocation of Crowd Contributions for Sentiment Analysis during the 2016 U.S. Presidential Election
We explore two dynamic-allocation methods: (1) The number of workers queried to label a tweet is computed offline based on the predicted difficulty of discerning the sentiment of a particular tweet.
Automating Image Analysis by Annotating Landmarks with Deep Neural Networks
Image and video analysis is often a crucial step in the study of animal behavior and kinematics.
Salient Object Subitizing
We study the problem of Salient Object Subitizing, i. e. predicting the existence and the number of salient objects in an image using holistic cues.
Discovering Useful Parts for Pose Estimation in Sparsely Annotated Datasets
Our work introduces a novel way to increase pose estimation accuracy by discovering parts from unannotated regions of training images.
BUOCA: Budget-Optimized Crowd Worker Allocation
We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling.
Pull the Plug? Predicting If Computers or Humans Should Segment Images
We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods.
Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks
Leveraging recent work on learning Bayesian neural networks, we build fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy.
Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch
We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods.
Deep Neural Network for Semantic-based Text Recognition in Images
A text-recognition network interprets isolated words.
Detecting Frames in News Headlines and Its Application to Analyzing News Framing Trends Surrounding U.S. Gun Violence
We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U. S. between 2016 and 2018.
A method for detecting text of arbitrary shapes in natural scenes that improves text spotting
Our text spotting framework, called UHTA, combines UHT with the state-of-the-art text recognition system ASTER.
Ranked #11 on
Scene Text Detection
on Total-Text
Performance Comparison of Crowdworkers and NLP Tools on Named-Entity Recognition and Sentiment Analysis of Political Tweets
We report results of a comparison of the accuracy of crowdworkers and seven Natural Language Processing (NLP) toolkits in solving two important NLP tasks, named-entity recognition (NER) and entity-level sentiment (ELS) analysis.
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS).
Multi-Label and Multilingual News Framing Analysis
News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation.
SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition with Distractors
We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications.
Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation
Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data.
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage
Such perspectives are called “frames” in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article.
An Unsupervised Approach to Discover Media Frames
We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.
BU-NEmo: an Affective Dataset of Gun Violence News
As such, we introduce a U. S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings.
BU-CVKit: Extendable Computer Vision Framework for Species Independent Tracking and Analysis
A major bottleneck of interdisciplinary computer vision (CV) research is the lack of a framework that eases the reuse and abstraction of state-of-the-art CV models by CV and non-CV researchers alike.
