Search Results for author:
Found 15 papers, 5 papers with code
MOSO: Decomposing MOtion, Scene and Object for Video Prediction
Experimental results demonstrate that our method achieves new state-of-the-art performance on five challenging benchmarks for video prediction and unconditional video generation: BAIR, RoboNet, KTH, KITTI and UCF101.
Beta-Sorted Portfolios
Beta-sorted portfolios -- portfolios comprised of assets with similar covariation to selected risk factors -- are a popular tool in empirical finance to analyze models of (conditional) expected returns.
Policy Choice in Time Series by Empirical Welfare Maximization
This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate time series.
Learning Disentangled Representation for One-shot Progressive Face Swapping
In addition, semantic information is introduced into the semantic-guided fusion module to control the swapped area and model the pose and expression more accurately.
A semiparametric approach for interactive fixed effects panel data models
We derive its asymptotic properties by finding out that the limiting distribution has a discontinuity, depending on the explanatory power of our basis functions which is expressed by the variance of the error of the factor loadings.
Dynamic Network Quantile Regression Model
We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information.
Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing
Compared with image scene parsing, video scene parsing introduces temporal information, which can effectively improve the consistency and accuracy of prediction.
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources.
Ranked #1 on
Image Retrieval
on Localized Narratives
Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network
To tackle these challenges, we propose a novel Semantic-Driven Generative Adversarial Network (SDGAN) which embeds global structure-level style injection and local class-level knowledge re-weighting.
Learning Network with Focally Sparse Structure
We uncover the network effect with a flexible sparse deviation from a predetermined adjacency matrix.
Temporal Memory Attention for Video Semantic Segmentation
Video semantic segmentation requires to utilize the complex temporal relations between frames of the video sequence.
Ranked #1 on
Video Semantic Segmentation
on CamVid
HAIR: Hierarchical Visual-Semantic Relational Reasoning for Video Question Answering
Specifically, we present a novel graph memory mechanism to perform relational reasoning, and further develop two types of graph memory: a) visual graph memory that leverages visual information of video for relational reasoning; b) semantic graph memory that is specifically designed to explicitly leverage semantic knowledge contained in the classes and attributes of video objects, and perform relational reasoning in the semantic space.
AutoCaption: Image Captioning with Neural Architecture Search
The whole process involves a visual understanding module and a language generation module, which brings more challenges to the design of deep neural networks than other tasks.
Pricing Cryptocurrency Options
We show how the proposed pricing mechanism underlines the importance of jumps in cryptocurrency markets.
Language-Driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model
Current studies on action detection in untrimmed videos are mostly designed for action classes, where an action is described at word level such as jumping, tumbling, swing, etc.
