Search Results for author:
Found 3 papers, 3 papers with code
Adaptive Local Basis Functions for Shape Completion
Quantitative and qualitative experiments demonstrate that our method outperforms the state-of-the-art methods in shape completion, detail preservation, generalization to unseen geometries, and computational cost.
Unsupervised Image Generation with Infinite Generative Adversarial Networks
Image generation has been heavily investigated in computer vision, where one core research challenge is to generate images from arbitrarily complex distributions with little supervision.
EmbedMask: Embedding Coupling for One-stage Instance Segmentation
The pixel-level clustering enables EmbedMask to generate high-resolution masks without missing details from repooling, and the existence of proposal embedding simplifies and strengthens the clustering procedure to achieve high speed with higher performance than segmentation-based methods.
Ranked #87 on
Instance Segmentation
on COCO test-dev
