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Found 10 papers, 6 papers with code
Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation
In this paper, we propose a new framework Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation (DTS-VIBE), to generate 3D human pose and mesh from RGB videos.
Virtual Multi-Modality Self-Supervised Foreground Matting for Human-Object Interaction
In this paper, we propose a Virtual Multi-modality Foreground Matting (VMFM) method to learn human-object interactive foreground (human and objects interacted with him or her) from a raw RGB image.
Implicit Normalizing Flows
Through theoretical analysis, we show that the function space of ImpFlow is strictly richer than that of ResFlows.
DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild
Experimental results show that DFEW is a well-designed and challenging database, and the proposed EC-STFL can promisingly improve the performance of existing spatiotemporal deep neural networks in coping with the problem of dynamic FER in the wild.
Discriminative Multi-modality Speech Recognition
Vision is often used as a complementary modality for audio speech recognition (ASR), especially in the noisy environment where performance of solo audio modality significantly deteriorates.
Ranked #6 on
Lipreading
on Lip Reading in the Wild
Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild
To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios.
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations.
Ranked #20 on
Image Generation
on CIFAR-10
(bits/dimension metric)
Dually Supervised Feature Pyramid for Object Detection and Segmentation
Feature pyramid architecture has been broadly adopted in object detection and segmentation to deal with multi-scale problem.
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling
From traditional Web search engines to virtual assistants and Web accelerators, services that rely on online information need to continually keep track of remote content changes by explicitly requesting content updates from remote sources (e. g., web pages).
Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio.