463 citations · 5.1k across the 190 of their papers we have counts for
27 papers · 1 filter
Pushing the Efficiency Limit Using Structured Sparse Convolutions
Vinay Kumar Verma, Nikhil Mehta, Shijing Si +2
Weight pruning is among the most popular approaches for compressing deep convolutional neural networks. Recent work suggests that in a randomly initialized deep neural network, the…
Towards Fair Federated Learning with Zero-Shot Data Augmentation
Weituo Hao, Mostafa El-Khamy, Jungwon Lee +4
Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the…
Malignancy Prediction and Lesion Identification from Clinical Dermatological Images
Meng Xia, Meenal K. Kheterpal, Samantha C. Wong +4
We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images, which can be indistinctly acquired via smartphone or dermosc…
Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning
Vinay Kumar Verma, Kevin Liang, Nikhil Mehta +1
Zero-shot learning (ZSL) has been shown to be a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges still…
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Zidi Xiu, Junya Chen, Ricardo Henao +3
Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of pr…
Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images
John B. Sigman, Gregory P. Spell, Kevin J Liang +1
Recently, progress has been made in the supervised training of Convolutional Object Detectors (e.g. Faster R-CNN) for threat recognition in carry-on luggage using X-ray images. Thi…
Weakly supervised cross-domain alignment with optimal transport
Siyang Yuan, Ke Bai, Liqun Chen +6
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural langu…
GAN Memory with No Forgetting
Yulai Cong, Miaoyun Zhao, Jianqiao Li +2
As a fundamental issue in lifelong learning, catastrophic forgetting is directly caused by inaccessible historical data; accordingly, if the data (information) were memorized perfe…
On Leveraging Pretrained GANs for Generation with Limited Data
Miaoyun Zhao, Yulai Cong, Lawrence Carin
Recent work has shown generative adversarial networks (GANs) can generate highly realistic images, that are often indistinguishable (by humans) from real images. Most images so gen…
Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training
Weituo Hao, Chunyuan Li, Xiujun Li +2
Learning to navigate in a visual environment following natural-language instructions is a challenging task, because the multimodal inputs to the agent are highly variable, and the…
Object Detection as a Positive-Unlabeled Problem
Yuewei Yang, Kevin J Liang, Lawrence Carin
As with other deep learning methods, label quality is important for learning modern convolutional object detectors. However, the potentially large number and wide diversity of obje…
Toward Automatic Threat Recognition for Airport X-ray Baggage Screening with Deep Convolutional Object Detection
Kevin J Liang, John B. Sigman, Gregory P. Spell +5
For the safety of the traveling public, the Transportation Security Administration (TSA) operates security checkpoints at airports in the United States, seeking to keep dangerous i…
LMVP: Video Predictor with Leaked Motion Information
Dong Wang, Yitong Li, Wei Cao +3
We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs. The motion is modeled by a newly pr…
Adaptation Across Extreme Variations using Unlabeled Domain Bridges
Shuyang Dai, Kihyuk Sohn, Yi-Hsuan Tsai +2
We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter and…
Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images
David Dov, Shahar Kovalsky, Jonathan Cohen +3
We consider preoperative prediction of thyroid cancer based on ultra-high-resolution whole-slide cytopathology images. Inspired by how human experts perform diagnosis, our approach…
StoryGAN: A Sequential Conditional GAN for Story Visualization
Yitong Li, Zhe Gan, Yelong Shen +6
We propose a new task, called Story Visualization. Given a multi-sentence paragraph, the story is visualized by generating a sequence of images, one for each sentence. In contrast…
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Xinyuan Zhang, Xin Yuan, Lawrence Carin
Low-rank signal modeling has been widely leveraged to capture non-local correlation in image processing applications. We propose a new method that employs low-rank tensor factor an…
An inner-loop free solution to inverse problems using deep neural networks
Qi Wei, Kai Fan, Lawrence Carin +1
We propose a new method that uses deep learning techniques to accelerate the popular alternating direction method of multipliers (ADMM) solution for inverse problems. The ADMM upda…
Semantic Compositional Networks for Visual Captioning
Zhe Gan, Chuang Gan, Xiaodong He +5
A Semantic Compositional Network (SCN) is developed for image captioning, in which semantic concepts (i.e., tags) are detected from the image, and the probability of each tag is us…
Adaptive Feature Abstraction for Translating Video to Text
Yunchen Pu, Martin Renqiang Min, Zhe Gan +1
Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video features. However, the variable context-dependent s…
Joint System and Algorithm Design for Computationally Efficient Fan Beam Coded Aperture X-ray Coherent Scatter Imaging
Ikenna Odinaka, Joseph A. O'Sullivan, David G. Politte +8
In x-ray coherent scatter tomography, tomographic measurements of the forward scatter distribution are used to infer scatter densities within a volume. A radiopaque 2D pattern plac…
A Deep Generative Deconvolutional Image Model
Yunchen Pu, Xin Yuan, Andrew Stevens +2
A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic {\em unpooling} is e…
Compressive Hyperspectral Imaging with Side Information
Xin Yuan, Tsung-Han Tsai, Ruoyu Zhu +3
A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements.The wavelength-dependent data are coded and then super…
Tree-Structure Bayesian Compressive Sensing for Video
Xin Yuan, Patrick Llull, David J. Brady +1
A Bayesian compressive sensing framework is developed for video reconstruction based on the color coded aperture compressive temporal imaging (CACTI) system. By exploiting the thre…