Vision Transformers (ViTs) have recently dominated a range of computer vision tasks, yet it suffers from low training data efficiency and inferior local semantic representation capability without appropriate inductive bias.
In this work, we propose a framework for single-view hand mesh reconstruction, which can simultaneously achieve high reconstruction accuracy, fast inference speed, and temporal coherence.
To further improve the performance of these tasks, we propose a novel Hand Image Understanding (HIU) framework to extract comprehensive information of the hand object from a single RGB image, by jointly considering the relationships between these tasks.
Earthquake early warning systems are required to report earthquake locations and magnitudes as quickly as possible before the damaging S wave arrival to mitigate seismic hazards.
This paper presents a differentially private algorithm for linear regression learning in a decentralized fashion.
To relax their privacy concerns about ID privacy, this paper formally proposes the notion of asymmetrical vertical federated learning and illustrates the way to protect sample IDs.
Neural Architecture Search (NAS) has shown great potentials in automatically designing scalable network architectures for dense image predictions.
Ranked #9 on Semantic Segmentation on Cityscapes test (using extra training data)
They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services.
In this paper, we present a HAnd Mesh Recovery (HAMR) framework to tackle the problem of reconstructing the full 3D mesh of a human hand from a single RGB image.