Search Results for author:
Found 10 papers, 2 papers with code
TUVF: Learning Generalizable Texture UV Radiance Fields
This allows the texture to be disentangled from the underlying shape and transferable to other shapes that share the same UV space, i. e., from the same category.
Autoregressive 3D Shape Generation via Canonical Mapping
With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation.
Learning 3D Dense Correspondence via Canonical Point Autoencoder
We propose a canonical point autoencoder (CPAE) that predicts dense correspondences between 3D shapes of the same category.
Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization
To preserve the knowledge we learn from previous instances, we proposed a method to protect the path by restricting the gradient updates of one instance from overriding past updates calculated from previous instances if these instances are not similar.
InstaNAS: Instance-aware Neural Architecture Search
Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives such as accuracy.
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information
The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver.
Searching Toward Pareto-Optimal Device-Aware Neural Architectures
Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding.
DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures
We propose DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures, optimizing for both device-related (e. g., inference time and memory usage) and device-agnostic (e. g., accuracy and model size) objectives.
