Search Results for author:
Found 16 papers, 6 papers with code
GWQ: Gradient-Aware Weight Quantization for Large Language Models
Compared to current quantization methods, GWQ can be applied to multiple language models and achieves lower PPL on the WikiText2 and C4 dataset.
TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation
We present TANGO, a framework for generating co-speech body-gesture videos.
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.
Optimizing Cooperative path-finding: A Scalable Multi-Agent RRT* with Dynamic Potential Fields
Cooperative path-finding in multi-agent systems demands scalable solutions to navigate agents from their origins to destinations without conflict.
