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Activity Graph Transformer for Temporal Action Localization
We introduce Activity Graph Transformer, an end-to-end learnable model f...
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Neural fidelity warping for efficient robot morphology design
We consider the problem of optimizing a robot morphology to achieve the ...
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MCMI: Multi-Cycle Image Translation with Mutual Information Constraints
We present a mutual information-based framework for unsupervised image-t...
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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...
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Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Normalizing flows transform a simple base distribution into a complex ta...
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Variational Hyper RNN for Sequence Modeling
In this work, we propose a novel probabilistic sequence model that excel...
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Adapting Grad-CAM for Embedding Networks
The gradient-weighted class activation mapping (Grad-CAM) method can fai...
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Point Process Flows
Event sequences can be modeled by temporal point processes (TPPs) to cap...
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Graph Generation with Variational Recurrent Neural Network
Generating graph structures is a challenging problem due to the diverse ...
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Policy Message Passing: A New Algorithm for Probabilistic Graph Inference
A general graph-structured neural network architecture operates on graph...
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Relational Graph Learning for Crowd Navigation
We present a relational graph learning approach for robotic crowd naviga...
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COPHY: Counterfactual Learning of Physical Dynamics
Understanding causes and effects in mechanical systems is an essential c...
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Continuous Graph Flow for Flexible Density Estimation
In this paper, we propose Continuous Graph Flow, a generative continuous...
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LayoutVAE: Stochastic Scene Layout Generation from a Label Set
Recently there is an increasing interest in scene generation within the ...
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Lifelong GAN: Continual Learning for Conditional Image Generation
Lifelong learning is challenging for deep neural networks due to their s...
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Similarity-Preserving Knowledge Distillation
Knowledge distillation is a widely applicable technique for training a s...
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A Variational Auto-Encoder Model for Stochastic Point Processes
We propose a novel probabilistic generative model for action sequences. ...
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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...
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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...
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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...
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Object Level Visual Reasoning in Videos
Human activity recognition is typically addressed by training models to ...
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Distribution Aware Active Learning
Discriminative learning machines often need a large set of labeled sampl...
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Probabilistic Video Generation using Holistic Attribute Control
Videos express highly structured spatio-temporal patterns of visual data...
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Structured Label Inference for Visual Understanding
Visual data such as images and videos contain a rich source of structure...
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Sparsely Connected Convolutional Networks
Residual learning with skip connections permits training ultra-deep neur...
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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...
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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...
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Hierarchical Label Inference for Video Classification
Videos are a rich source of high-dimensional structured data, with a wid...
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Learning to Learn from Noisy Web Videos
Understanding the simultaneously very diverse and intricately fine-grain...
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Active Learning for Structured Prediction from Partially Labeled Data
We propose a general purpose active learning algorithm for structured pr...
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Learning Person Trajectory Representations for Team Activity Analysis
Activity analysis in which multiple people interact across a large space...
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Generic Tubelet Proposals for Action Localization
We develop a novel framework for action localization in videos. We propo...
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LabelBank: Revisiting Global Perspectives for Semantic Segmentation
Semantic segmentation requires a detailed labeling of image pixels by ob...
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Recalling Holistic Information for Semantic Segmentation
Semantic segmentation requires a detailed labeling of image pixels by ob...
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Hierarchical Deep Temporal Models for Group Activity Recognition
In this paper we present an approach for classifying the activity perfor...
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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...
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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...
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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...
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Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Every moment counts in action recognition. A comprehensive understanding...
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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...
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Deep Structured Models For Group Activity Recognition
This paper presents a deep neural-network-based hierarchical graphical m...
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Learning Temporal Embeddings for Complex Video Analysis
In this paper, we propose to learn temporal embeddings of video frames f...
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Discovering Human Interactions in Videos with Limited Data Labeling
We present a novel approach for discovering human interactions in videos...
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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...
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Hierarchical Maximum-Margin Clustering
We present a hierarchical maximum-margin clustering method for unsupervi...
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Multiple Instance Learning by Discriminative Training of Markov Networks
We introduce a graphical framework for multiple instance learning (MIL) ...
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