Search Results for author:
Found 9 papers, 2 papers with code
Monocular 3D Object Detection via Feature Domain Adaptation
Monocular 3D object detection is a challenging task due to unreliable depth, resulting in a distinct performance gap between monocular and LiDAR-based approaches.
Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time.
Talking-head Generation with Rhythmic Head Motion
When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys prosodic information.
Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction
However, existing GCN-based ride-hailing demand prediction methods only assign the same importance to different neighbor regions, and maintain a fixed graph structure with static spatial relationships throughout the timeline when extracting the irregular non-Euclidean spatial correlations.
STDI-Net: Spatial-Temporal Network with Dynamic Interval Mapping for Bike Sharing Demand Prediction
As an economical and healthy mode of shared transportation, Bike Sharing System (BSS) develops quickly in many big cities.
What comprises a good talking-head video generation?: A Survey and Benchmark
In this work, we present a carefully-designed benchmark for evaluating talking-head video generation with standardized dataset pre-processing strategies.
Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation
In this paper, we propose a confidence segmentation (ConfSeg) module that builds confidence score for each pixel in CAM without introducing additional hyper-parameters.
Weakly Supervised Localization Using Background Images
Weakly Supervised Object Localization (WSOL) methodsusually rely on fully convolutional networks in order to ob-tain class activation maps(CAMs) of targeted labels.
How to Make a BLT Sandwich? Learning to Reason towards Understanding Web Instructional Videos
Furthermore, we study multiple modalities including description and transcripts for the purpose of boosting video understanding.
