Search Results for author:
Found 10 papers, 2 papers with code
Cubify Anything: Scaling Indoor 3D Object Detection
Furthermore, by pre-training on CA-1M, CuTR can outperform point-based methods on a more diverse variant of SUN RGB-D - supporting the notion that while inductive biases in 3D are useful at the smaller sizes of existing datasets, they fail to scale to the data-rich regime of CA-1M.
Instance Segmentation With Mask-Supervised Polygonal Boundary Transformers
In this paper, we present an end-to-end instance segmentation method that regresses a polygonal boundary for each object instance.
Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning
While remarkable progress in imbalanced supervised learning has been made recently, less attention has been given to the setting of imbalanced semi-supervised learning (SSL) where not only is a few labeled data provided, but the underlying data distribution can be severely imbalanced.
Multimodal Attention for Layout Synthesis in Diverse Domains
We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents and 3D objects.
LayoutTransformer: Layout Generation and Completion with Self-attention
Generating a new layout or extending an existing layout requires understanding the relationships between these primitives.
Unaligned Image-to-Sequence Transformation with Loop Consistency
We tackle the problem of modeling sequential visual phenomena.
Learning Instance Occlusion for Panoptic Segmentation
Panoptic segmentation requires segments of both "things" (countable object instances) and "stuff" (uncountable and amorphous regions) within a single output.
Ranked #22 on
Panoptic Segmentation
on COCO test-dev
Introspective Neural Networks for Generative Modeling
We study unsupervised learning by developing a generative model built from progressively learned deep convolutional neural networks.
Introspective Generative Modeling: Decide Discriminatively
We study unsupervised learning by developing introspective generative modeling (IGM) that attains a generator using progressively learned deep convolutional neural networks.
Introspective Classification with Convolutional Nets
We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural networks empowered with generative capabilities.
