Search Results for author:
Found 6 papers, 1 papers with code
ProMotion: Prototypes As Motion Learners
We capitalize on a dual mechanism involving the feature denoiser and the prototypical learner to decipher the intricacies of motion.
Prototypical Transformer as Unified Motion Learners
In this work, we introduce the Prototypical Transformer (ProtoFormer), a general and unified framework that approaches various motion tasks from a prototype perspective.
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking
In Virtual Reality (VR), adversarial attack remains a significant security threat.
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS).
TransFlow: Transformer as Flow Learner
Optical flow is an indispensable building block for various important computer vision tasks, including motion estimation, object tracking, and disparity measurement.
Unsupervised Simultaneous Depth-from-defocus and Depth-from-focus
The proposed network is able to learn optimal depth mapping from the information contained in the blurring of a single image, generate a simulated image focal stack and all-in-focus image, and train a depth estimator from an image focal stack.
