Abstract Three-dimensional (3D) object recognition is widely used in automated driving, medical image analysis, virtual/augmented reality, artificial intelligence robots, and other …
Although the recent rapid evolution of 3D generative neural networks greatly improves 3D shape generation, it is still not convenient for ordinary users to create 3D shapes and control …
In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled …
In recent years, many deep learning techniques have been applied to the image inpainting problem: the task of filling incomplete regions of an image. However, these models struggle …
Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable …
Abstract Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets …
Y Li, R Bu, M Sun, W Wu, X Di… - Advances in neural …, 2018 - proceedings.neurips.cc
We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local …
With the advent of generative adversarial networks, synthesizing images from text descriptions has recently become an active research area. It is a flexible and intuitive way for …
Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision. The typical way of conducting transfer learning with deep …