Search Results for author:
Found 8 papers, 3 papers with code
TTMFN: Two-stream Transformer-based Multimodal Fusion Network for Survival Prediction
In TTMFN, we present a two-stream multimodal co-attention transformer module to take full advantage of the complex relationships between different modalities and the potential connections within the modalities.
VTP: Volumetric Transformer for Multi-view Multi-person 3D Pose Estimation
The proposed VTP framework integrates the high performance of the transformer with volumetric representations, which can be used as a good alternative to the convolutional backbones.
Ranked #4 on
3D Human Pose Estimation
on Panoptic
(using extra training data)
Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows
In this paper, a novel deep learning framework is proposed for temporal super-resolution simulation of blood vessel flows, in which a high-temporal-resolution time-varying blood vessel flow simulation is generated from a low-temporal-resolution flow simulation result.
Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle Tracking
In this paper, we try to explore the significance of motion patterns for vehicle tracking without appearance information.
LASOR: Learning Accurate 3D Human Pose and Shape Via Synthetic Occlusion-Aware Data and Neural Mesh Rendering
The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild.
Ranked #2 on
3D Human Shape Estimation
on SSP-3D
Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking
To address such common challenges in most of the existing trackers, in this paper, a tracklet booster algorithm is proposed, which can be built upon any other tracker.
Exploring Severe Occlusion: Multi-Person 3D Pose Estimation with Gated Convolution
Videos that contain multiple potentially occluded people captured from freely moving monocular cameras are very common in real-world scenarios, while 3D HPE for such scenarios is quite challenging, partially because there is a lack of such data with accurate 3D ground truth labels in existing datasets.
Exploit the Connectivity: Multi-Object Tracking with TrackletNet
Multi-object tracking (MOT) is an important and practical task related to both surveillance systems and moving camera applications, such as autonomous driving and robotic vision.
Ranked #19 on
Multi-Object Tracking
on MOT16
