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Found 12 papers, 6 papers with code
Bidirectional Generative Pre-training for Improving Time Series Representation Learning
Learning time-series representations for discriminative tasks has been a long-standing challenge.
TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces
This report describes the solution that secured the first place in the "View Synthesis Challenge for Human Heads (VSCHH)" at the ICCV 2023 workshop.
NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos
In this paper, we aim to model 3D scene dynamics from multi-view videos.
Extrapolatable Transformer Pre-training for Ultra Long Time-Series Forecasting
This underscores the limitations of the existing transformer-based architectures, particularly their scalability to handle large-scale data and ability to capture long-term temporal dependencies.
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics.
Ranked #1 on
Unsupervised Monocular Depth Estimation
on KITTI-C
(using extra training data)
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
In this paper, we study the problem of 3D object segmentation from raw point clouds.
ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation
We present a GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions.
Multi-scale Matching Networks for Semantic Correspondence
We follow the coarse-to-fine matching strategy and build a top-down feature and matching enhancement scheme that is coupled with the multi-scale hierarchy of deep convolutional neural networks.
Ranked #11 on
Semantic correspondence
on SPair-71k
Supervised multi-specialist topic model with applications on large-scale electronic health record data
Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine.
Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation
In recognition-based action interaction, robots' responses to human actions are often pre-designed according to recognized categories and thus stiff.
Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction
Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction.
