565 citations · 1.1k across the 27 of their papers we have counts for
28 papers
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization
Xueqing Deng, Dawei Sun, Shawn Newsam +1
Recent Knowledge distillation (KD) studies show that different manually designed schemes impact the learned results significantly. Yet, in KD, automatically searching an optimal di…
NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night
Xueqing Deng, Peng Wang, Xiaochen Lian +1
The semantic segmentation of nighttime scenes is a challenging problem that is key to impactful applications like self-driving cars. Yet, it has received little attention compared…
Image Search with Text Feedback by Additive Attention Compositional Learning
Yuxin Tian, Shawn Newsam, Kofi Boakye
Effective image retrieval with text feedback stands to impact a range of real-world applications, such as e-commerce. Given a source image and text feedback that describes the desi…
AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation
Xueqing Deng, Yi Zhu, Yuxin Tian +1
Neural network-based semantic segmentation has achieved remarkable results when large amounts of annotated data are available, that is, in the supervised case. However, such data i…
Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery
Xueqing Deng, Yi Zhu, Yuxin Tian +1
Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully conne…
Motion-Aware Feature for Improved Video Anomaly Detection
Yi Zhu, Shawn Newsam
Motivated by our observation that motion information is the key to good anomaly detection performance in video, we propose a temporal augmented network to learn a motion-aware feat…
Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
Xueqing Deng, Yi Zhu, Shawn Newsam
This paper develops a deep-learning framework to synthesize a ground-level view of a location given an overhead image. We propose a novel conditional generative adversarial network…
Improving Semantic Segmentation via Video Propagation and Label Relaxation
Yi Zhu, Karan Sapra, Fitsum A. Reda +4
Semantic segmentation requires large amounts of pixel-wise annotations to learn accurate models. In this paper, we present a video prediction-based methodology to scale up training…
Random Temporal Skipping for Multirate Video Analysis
Yi Zhu, Shawn Newsam
Current state-of-the-art approaches to video understanding adopt temporal jittering to simulate analyzing the video at varying frame rates. However, this does not work well for mul…
Gated Transfer Network for Transfer Learning
Yi Zhu, Jia Xue, Shawn Newsam
Deep neural networks have led to a series of breakthroughs in computer vision given sufficient annotated training datasets. For novel tasks with limited labeled data, the prevalent…
What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks
Xueqing Deng, Yi Zhu, Shawn Newsam
This paper investigates conditional generative adversarial networks (cGANs) to overcome a fundamental limitation of using geotagged media for geographic discovery, namely its spars…
Learning Optical Flow via Dilated Networks and Occlusion Reasoning
Yi Zhu, Shawn Newsam
Despite the significant progress that has been made on estimating optical flow recently, most estimation methods, including classical and deep learning approaches, still have diffi…
Towards Universal Representation for Unseen Action Recognition
Yi Zhu, Yang Long, Yu Guan +2
Unseen Action Recognition (UAR) aims to recognise novel action categories without training examples. While previous methods focus on inner-dataset seen/unseen splits, this paper pr…
Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images
Yi Zhu, Xueqing Deng, Shawn Newsam
We perform fine-grained land use mapping at the city scale using ground-level images. Mapping land use is considerably more difficult than mapping land cover and is generally not p…
Spatial Morphing Kernel Regression For Feature Interpolation
Xueqing Deng, Yi Zhu, Shawn Newsam
In recent years, geotagged social media has become popular as a novel source for geographic knowledge discovery. Ground-level images and videos provide a different perspective than…
Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use
Xueqing Deng, Shawn Newsam
This paper performs a quantitative comparison of open-source data available on the Internet for the fine-grain mapping of land use. Three points of interest (POI) data sources--Goo…
DenseNet for Dense Flow
Yi Zhu, Shawn Newsam
Classical approaches for estimating optical flow have achieved rapid progress in the last decade. However, most of them are too slow to be applied in real-time video analysis. Due…
PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval
Weixun Zhou, Shawn Newsam, Congmin Li +1
Remote sensing image retrieval(RSIR), which aims to efficiently retrieve data of interest from large collections of remote sensing data, is a fundamental task in remote sensing. Ov…
Large-Scale Mapping of Human Activity using Geo-Tagged Videos
Yi Zhu, Sen Liu, Shawn Newsam
This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analy…
UC Merced Submission to the ActivityNet Challenge 2016
Yi Zhu, Shawn Newsam, Zaikun Xu
This notebook paper describes our system for the untrimmed classification task in the ActivityNet challenge 2016. We investigate multiple state-of-the-art approaches for action rec…
Hidden Two-Stream Convolutional Networks for Action Recognition
Yi Zhu, Zhenzhong Lan, Shawn Newsam +1
Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical f…
Guided Optical Flow Learning
Yi Zhu, Zhenzhong Lan, Shawn Newsam +1
We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data. Supervised CNNs, due to their immense learning capacity, have shown superior p…
Deep Local Video Feature for Action Recognition
Zhenzhong Lan, Yi Zhu, Alexander G. Hauptmann
We investigate the problem of representing an entire video using CNN features for human action recognition. Currently, limited by GPU memory, we have not been able to feed a whole…
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning
Yi Zhu, Shawn Newsam
This paper studies the joint learning of action recognition and temporal localization in long, untrimmed videos. We employ a multi-task learning framework that performs the three h…