Search Results for author: Shawn Newsam

Found 27 papers, 4 papers with code

DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization

no code implementations12 Apr 2022 Xueqing Deng, Dawei Sun, Shawn Newsam, Peng Wang

Specifically, given a pair of student and teacher networks, DistPro first sets up a rich set of KD connection from the transmitting layers of the teacher to the receiving layers of the student, and in the meanwhile, various transforms are also proposed for comparing feature maps along its pathway for the distillation.

Knowledge Distillation Meta-Learning

NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night

1 code implementation12 Apr 2022 Xueqing Deng, Peng Wang, Xiaochen Lian, Shawn Newsam

Notably, NightLab contains models at two levels of granularity, i. e. image and regional, and each level is composed of light adaptation and segmentation modules.

Self-Driving Cars Semantic Segmentation

Image Search with Text Feedback by Additive Attention Compositional Learning

no code implementations8 Mar 2022 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.

Image Retrieval

AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation

no code implementations24 Jun 2021 Xueqing Deng, Yi Zhu, Yuxin Tian, Shawn Newsam

Neural network-based semantic segmentation has achieved remarkable results when large amounts of annotated data are available, that is, in the supervised case.

Neural Architecture Search Semantic Segmentation +1

Generalizing Deep Models for Overhead Image Segmentation Through Getis-Ord Gi* Pooling

no code implementations23 Dec 2019 Xueqing Deng, Yi Zhu, Yuxin Tian, Shawn Newsam

Inspired by this, we investigate methods to inform or guide deep learning models for geospatial image analysis to increase their performance when a limited amount of training data is available or when they are applied to scenarios other than which they were trained on.

Semantic Segmentation

Motion-Aware Feature for Improved Video Anomaly Detection

no code implementations24 Jul 2019 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 feature.

Action Recognition Anomaly Detection +1

Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery

no code implementations19 Feb 2019 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.

General Classification

Improving Semantic Segmentation via Video Propagation and Label Relaxation

3 code implementations CVPR 2019 Yi Zhu, Karan Sapra, Fitsum A. Reda, Kevin J. Shih, Shawn Newsam, Andrew Tao, Bryan Catanzaro

In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks.

 Ranked #1 on Semantic Segmentation on CamVid (using extra training data)

Semantic Segmentation Video Propagation

Random Temporal Skipping for Multirate Video Analysis

no code implementations30 Oct 2018 Yi Zhu, Shawn Newsam

However, this does not work well for multirate videos, in which actions or subactions occur at different speeds.

Action Recognition Frame +2

Gated Transfer Network for Transfer Learning

no code implementations30 Oct 2018 Yi Zhu, Jia Xue, Shawn Newsam

Deep neural networks have led to a series of breakthroughs in computer vision given sufficient annotated training datasets.

Transfer Learning

What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks

no code implementations13 Jun 2018 Xueqing Deng, Yi Zhu, Shawn Newsam

More significantly, we show the generated images are representative of the locations and that the representations learned by the cGANs are informative.

General Classification

Learning Optical Flow via Dilated Networks and Occlusion Reasoning

no code implementations7 May 2018 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 difficulty with multi-scale estimation, real-time computation, and/or occlusion reasoning.

Action Recognition Optical Flow Estimation

DenseNet for Dense Flow

no code implementations19 Jul 2017 Yi Zhu, Shawn Newsam

Classical approaches for estimating optical flow have achieved rapid progress in the last decade.

Motion Estimation Optical Flow Estimation

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

no code implementations24 Jun 2017 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.

PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval

no code implementations11 Jun 2017 Weixun Zhou, Shawn Newsam, Congmin Li, Zhenfeng Shao

Current benchmark datasets are deficient in that 1) they were originally collected for land use/land cover classification and not image retrieval, 2) they are relatively small in terms of the number of classes as well the number of sample images per class, and 3) the retrieval performance has saturated.

Image Retrieval

UC Merced Submission to the ActivityNet Challenge 2016

no code implementations11 Apr 2017 Yi Zhu, Shawn Newsam, Zaikun Xu

This notebook paper describes our system for the untrimmed classification task in the ActivityNet challenge 2016.

Action Recognition General Classification

Hidden Two-Stream Convolutional Networks for Action Recognition

3 code implementations2 Apr 2017 Yi Zhu, Zhenzhong Lan, Shawn Newsam, Alexander G. Hauptmann

State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs.

Action Recognition Optical Flow Estimation

Guided Optical Flow Learning

no code implementations8 Feb 2017 Yi Zhu, Zhenzhong Lan, Shawn Newsam, Alexander G. Hauptmann

We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data.

Image Reconstruction Optical Flow Estimation

Efficient Action Detection in Untrimmed Videos via Multi-Task Learning

no code implementations22 Dec 2016 Yi Zhu, Shawn Newsam

We employ a multi-task learning framework that performs the three highly related steps of action proposal, action recognition, and action localization refinement in parallel instead of the standard sequential pipeline that performs the steps in order.

Action Detection Action Recognition +3

Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

no code implementations10 Oct 2016 Weixun Zhou, Shawn Newsam, Congmin Li, Zhenfeng Shao

In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNN) for high-resolution remote sensing image retrieval (HRRSIR).

Image Retrieval

Spatio-Temporal Sentiment Hotspot Detection Using Geotagged Photos

no code implementations21 Sep 2016 Yi Zhu, Shawn Newsam

We perform spatio-temporal analysis of public sentiment using geotagged photo collections.

Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition

no code implementations15 Aug 2016 Yi Zhu, Shawn Newsam

This paper performs the first investigation into depth for large-scale human action recognition in video where the depth cues are estimated from the videos themselves.

Action Recognition

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