Search Results for author:
Found 9 papers, 4 papers with code
Separate-and-Enhance: Compositional Finetuning for Text2Image Diffusion Models
Despite recent significant strides achieved by diffusion-based Text-to-Image (T2I) models, current systems are still less capable of ensuring decent compositional generation aligned with text prompts, particularly for the multi-object generation.
Multi-task View Synthesis with Neural Radiance Fields
To tackle the MTVS problem, we propose MuvieNeRF, a framework that incorporates both multi-task and cross-view knowledge to simultaneously synthesize multiple scene properties.
Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields
Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception.
Discovering Objects that Can Move
Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects.
Generative Modeling for Multitask Visual Learning
Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images.
Generative Modeling for Multi-task Visual Learning
Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images.
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints.
Deep Learning-Based Strategy for Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography
Deep learning model trained by imbalanced data may not work satisfactorily since it could be determined by major classes and thus may ignore the classes with small amount of data.
