Greg Mori

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Simon Fraser University ∙ Borealis AI

Professor and Director of School of Computing Science at Simon Fraser University

TD-GEN: Graph Generation With Tree Decomposition

We propose TD-GEN, a graph generation framework based on tree decomposit...
06/20/2021 ∙ by Hamed Shirzad, et al. ∙ 0 ∙ share

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Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation

Humans accumulate knowledge in a lifelong fashion. Modern deep neural ne...
04/24/2021 ∙ by Mengyao Zhai, et al. ∙ 0 ∙ share

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Adaptive Appearance Rendering

We propose an approach to generate images of people given a desired appe...
04/24/2021 ∙ by Mengyao Zhai, et al. ∙ 4 ∙ share

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Learning Discriminative Prototypes with Dynamic Time Warping

Dynamic Time Warping (DTW) is widely used for temporal data processing. ...
03/17/2021 ∙ by Xiaobin Chang, et al. ∙ 0 ∙ share

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Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data

Learning from heterogeneous data poses challenges such as combining data...
02/25/2021 ∙ by Yu Gong, et al. ∙ 0 ∙ share

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Activity Graph Transformer for Temporal Action Localization

We introduce Activity Graph Transformer, an end-to-end learnable model f...
01/21/2021 ∙ by Megha Nawhal, et al. ∙ 3 ∙ share

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Neural fidelity warping for efficient robot morphology design

We consider the problem of optimizing a robot morphology to achieve the ...
12/08/2020 ∙ by Sha Hu, et al. ∙ 0 ∙ share

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MCMI: Multi-Cycle Image Translation with Mutual Information Constraints

We present a mutual information-based framework for unsupervised image-t...
07/06/2020 ∙ by Xiang Xu, et al. ∙ 5 ∙ share

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House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation

This paper proposes a novel graph-constrained generative adversarial net...
03/16/2020 ∙ by Nelson Nauata, et al. ∙ 0 ∙ share

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Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows

Normalizing flows transform a simple base distribution into a complex ta...
02/24/2020 ∙ by Ruizhi Deng, et al. ∙ 7 ∙ share

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Variational Hyper RNN for Sequence Modeling

In this work, we propose a novel probabilistic sequence model that excel...
02/24/2020 ∙ by Ruizhi Deng, et al. ∙ 23 ∙ share

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Adapting Grad-CAM for Embedding Networks

The gradient-weighted class activation mapping (Grad-CAM) method can fai...
01/17/2020 ∙ by Lei Chen, et al. ∙ 9 ∙ share

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Point Process Flows

Event sequences can be modeled by temporal point processes (TPPs) to cap...
10/18/2019 ∙ by Nazanin Mehrasa, et al. ∙ 0 ∙ share

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Graph Generation with Variational Recurrent Neural Network

Generating graph structures is a challenging problem due to the diverse ...
10/02/2019 ∙ by Shih-Yang Su, et al. ∙ 0 ∙ share

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Policy Message Passing: A New Algorithm for Probabilistic Graph Inference

A general graph-structured neural network architecture operates on graph...
09/29/2019 ∙ by Zhiwei Deng, et al. ∙ 0 ∙ share

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Relational Graph Learning for Crowd Navigation

We present a relational graph learning approach for robotic crowd naviga...
09/28/2019 ∙ by Changan Chen, et al. ∙ 0 ∙ share

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COPHY: Counterfactual Learning of Physical Dynamics

Understanding causes and effects in mechanical systems is an essential c...
09/26/2019 ∙ by Fabien Baradel, et al. ∙ 8 ∙ share

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Continuous Graph Flow for Flexible Density Estimation

In this paper, we propose Continuous Graph Flow, a generative continuous...
08/07/2019 ∙ by Zhiwei Deng, et al. ∙ 21 ∙ share

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LayoutVAE: Stochastic Scene Layout Generation from a Label Set

Recently there is an increasing interest in scene generation within the ...
07/24/2019 ∙ by Akash Abdu Jyothi, et al. ∙ 3 ∙ share

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Lifelong GAN: Continual Learning for Conditional Image Generation

Lifelong learning is challenging for deep neural networks due to their s...
07/23/2019 ∙ by Mengyao Zhai, et al. ∙ 7 ∙ share

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Similarity-Preserving Knowledge Distillation

Knowledge distillation is a widely applicable technique for training a s...
07/23/2019 ∙ by Frederick Tung, et al. ∙ 0 ∙ share

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A Variational Auto-Encoder Model for Stochastic Point Processes

We propose a novel probabilistic generative model for action sequences. ...
04/05/2019 ∙ by Nazanin Mehrasa, et al. ∙ 0 ∙ share

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Learning a Deep ConvNet for Multi-label Classification with Partial Labels

Deep ConvNets have shown great performance for single-label image classi...
02/26/2019 ∙ by Thibaut Durand, et al. ∙ 0 ∙ share

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Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction

Numerous powerful point process models have been developed to understand...
08/13/2018 ∙ by Yatao Zhong, et al. ∙ 0 ∙ share

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Time Perception Machine: Temporal PointProcesses for the When, Where and What ofActivity Prediction

Numerous powerful point process models have been developed to understand...
08/13/2018 ∙ by Yatao Zhong, et al. ∙ 0 ∙ share

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Object Level Visual Reasoning in Videos

Human activity recognition is typically addressed by training models to ...
06/16/2018 ∙ by Fabien Baradel, et al. ∙ 0 ∙ share

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Distribution Aware Active Learning

Discriminative learning machines often need a large set of labeled sampl...
05/23/2018 ∙ by Arash Mehrjou, et al. ∙ 0 ∙ share

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Probabilistic Video Generation using Holistic Attribute Control

Videos express highly structured spatio-temporal patterns of visual data...
03/21/2018 ∙ by Jiawei He, et al. ∙ 2 ∙ share

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Structured Label Inference for Visual Understanding

Visual data such as images and videos contain a rich source of structure...
02/18/2018 ∙ by Nelson Nauata, et al. ∙ 0 ∙ share

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Sparsely Connected Convolutional Networks

Residual learning with skip connections permits training ultra-deep neur...
01/18/2018 ∙ by Ligeng Zhu, et al. ∙ 0 ∙ share

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Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering

We propose an approach for forecasting video of complex human activity i...
12/05/2017 ∙ by Mengyao Zhai, et al. ∙ 0 ∙ share

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Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization

When approaching a novel visual recognition problem in a specialized ima...
07/28/2017 ∙ by Frederick Tung, et al. ∙ 0 ∙ share

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Hierarchical Label Inference for Video Classification

Videos are a rich source of high-dimensional structured data, with a wid...
06/15/2017 ∙ by Nelson Nauata, et al. ∙ 0 ∙ share

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Learning to Learn from Noisy Web Videos

Understanding the simultaneously very diverse and intricately fine-grain...
06/09/2017 ∙ by Serena Yeung, et al. ∙ 0 ∙ share

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Active Learning for Structured Prediction from Partially Labeled Data

We propose a general purpose active learning algorithm for structured pr...
06/07/2017 ∙ by Mehran Khodabandeh, et al. ∙ 0 ∙ share

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Learning Person Trajectory Representations for Team Activity Analysis

Activity analysis in which multiple people interact across a large space...
06/03/2017 ∙ by Nazanin Mehrasa, et al. ∙ 0 ∙ share

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Generic Tubelet Proposals for Action Localization

We develop a novel framework for action localization in videos. We propo...
05/30/2017 ∙ by Jiawei He, et al. ∙ 0 ∙ share

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LabelBank: Revisiting Global Perspectives for Semantic Segmentation

Semantic segmentation requires a detailed labeling of image pixels by ob...
03/29/2017 ∙ by Hexiang Hu, et al. ∙ 0 ∙ share

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Recalling Holistic Information for Semantic Segmentation

Semantic segmentation requires a detailed labeling of image pixels by ob...
11/24/2016 ∙ by Hexiang Hu, et al. ∙ 0 ∙ share

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Hierarchical Deep Temporal Models for Group Activity Recognition

In this paper we present an approach for classifying the activity perfor...
07/09/2016 ∙ by Mostafa S. Ibrahim, et al. ∙ 0 ∙ share

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Deep Learning of Appearance Models for Online Object Tracking

This paper introduces a novel deep learning based approach for vision ba...
07/09/2016 ∙ by Mengyao Zhai, et al. ∙ 0 ∙ share

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End-to-end Learning of Action Detection from Frame Glimpses in Videos

In this work we introduce a fully end-to-end approach for action detecti...
11/22/2015 ∙ by Serena Yeung, et al. ∙ 0 ∙ share

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Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition

Rich semantic relations are important in a variety of visual recognition...
11/13/2015 ∙ by Zhiwei Deng, et al. ∙ 0 ∙ share

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Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

Every moment counts in action recognition. A comprehensive understanding...
07/21/2015 ∙ by Serena Yeung, et al. ∙ 0 ∙ share

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Pose Embeddings: A Deep Architecture for Learning to Match Human Poses

We present a method for learning an embedding that places images of huma...
07/01/2015 ∙ by Greg Mori, et al. ∙ 0 ∙ share

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Deep Structured Models For Group Activity Recognition

This paper presents a deep neural-network-based hierarchical graphical m...
06/12/2015 ∙ by Zhiwei Deng, et al. ∙ 0 ∙ share

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Learning Temporal Embeddings for Complex Video Analysis

In this paper, we propose to learn temporal embeddings of video frames f...
05/02/2015 ∙ by Vignesh Ramanathan, et al. ∙ 0 ∙ share

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Discovering Human Interactions in Videos with Limited Data Labeling

We present a novel approach for discovering human interactions in videos...
02/12/2015 ∙ by Mehran Khodabandeh, et al. ∙ 0 ∙ share

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Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel

Many visual recognition problems can be approached by counting instances...
02/06/2015 ∙ by Hossein Hajimirsadeghi, et al. ∙ 0 ∙ share

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Hierarchical Maximum-Margin Clustering

We present a hierarchical maximum-margin clustering method for unsupervi...
02/06/2015 ∙ by Guang-Tong Zhou, et al. ∙ 0 ∙ share

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Greg Mori

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