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Found 13 papers, 6 papers with code
Global-Aware Enhanced Spatial-Temporal Graph Recurrent Networks: A New Framework For Traffic Flow Prediction
A sequence-aware graph neural network is proposed and integrated into the Gated Recurrent Unit (GRU) to learn non-fixed graphs at different time steps and capture local temporal relationships.
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling
We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements.
Ranked #1 on
3D Face Animation
on BEAT2
Equal Incremental Cost-Based Optimization Method to Enhance Efficiency for IPOP-Type Converters
This paper addresses the issue of enhancing the efficiency of a multiple module system connected in parallel during operation and proposes an algorithm based on equal incremental cost for dynamic load allocation.
Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow Prediction
Traffic flow prediction is one of the most fundamental tasks of intelligent transportation systems.
Exploring the Mutual Influence between Self-Supervised Single-Frame and Multi-Frame Depth Estimation
Although both self-supervised single-frame and multi-frame depth estimation methods only require unlabeled monocular videos for training, the information they leverage varies because single-frame methods mainly rely on appearance-based features while multi-frame methods focus on geometric cues.
Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting
The key challenge is to effectively model complex spatial-temporal dependencies and correlations in modern traffic data.
Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving
Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue.
BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis
Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem due to the lack of available datasets, models and standard evaluation metrics.
Ranked #1 on
Gesture Generation
on BEAT
Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation
To solve these problems, this paper proposes (i) a new loss organization method which uses self-supervised heatmaps to reduce prediction contradictions and spatial-sequential attention to enhance networks' features extraction; (ii) a new combination of predictions composed by heatmaps, Part Affinity Fields (PAFs) and our block-inside offsets to fix pixel-level joints positions and further demonstrates the effectiveness of proposed loss function.
Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism
Speech production is a dynamic procedure, which involved multi human organs including the tongue, jaw and lips.
Learning unbiased zero-shot semantic segmentation networks via transductive transfer
Our method assumes that both the source images with full pixel-level labels and unlabeled target images are available during training.
Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network
Medication name inference is the task of mapping user friendly medication names from a free-form text to a concept in a normalized medication list.
