This is my reading note on CVPR 2021 tutorial on self supervised learning: Leave Those Nets Alone: Advances in Self-Supervised Learning and Data- and Label-Efficient Learning in An Imperfect World.
This is my reading note on CVPR 2021 Tutorial: Data- and Label-Efficient Learning in An Imperfect World. The original slides and videos are available online. Unsupervised domain adaption methods could be divided into the following groups:
Neural Lumigraph Rendering was accepted for CVPR 2021 Oral and best paper candidate. This paper proposes a method which performs on par with NeRF on view interpolation tasks while providing a high-quality 3D surface that can be directly exported for real-time rendering at test time. code is publically available.
NeX: Real-time View Synthesis with Neural Basis Expansion is paper from a group from Thailand and was accepted for CVPR 2021 Oral and best paper candidate. This paper proposes a method of synthesizing views from multiplane images, which is real time with view-dependent effects such as the reflection. The code is published is as well.
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving is paper from Uber ATG and was accepted for CVPR 2021 Oral and best paper candidate. This paper proposes a method of rendering videos which is realistic not in image quality but also in physical feasibilty.
This is the list of CVPR 2021 best paper candidates. Quite some of them are related to differental rendering or synthetic data.
Securities-Based Lending or 股权质押 refers to the practice of making loans using securities as collateral. Securities-based lending provides ready access to capital that can be used for almost any purpose such as buying real estate, purchasing property like jewelry or a sports car, or investing in a business. The only restrictions to this kind of lending are other securities-based transactions like buying shares or repaying a margin loan.
This is my literature survey of landmark detection and 3D reconstruction for cat and dog’s face.
This is my reading note for Residual Parameter Transfer for Deep Domain Adaptation, which is for domain adaption. Different from existing methods, which mostly aims to learn a network to adpat the (feature) of target domain to the source domain, this paper learns a transform on the parameters of network trained on source domain to the network for the target domain.