3 citations · 3 across the 3 of their papers we have counts for
8 papers · 1 filter
Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning
Jishnu Mukhoti, Tsung-Yu Lin, Omid Poursaeed +4
We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of t…
Robustness and Generalization via Generative Adversarial Training
Omid Poursaeed, Tianxing Jiang, Harry Yang +2
While deep neural networks have achieved remarkable success in various computer vision tasks, they often fail to generalize to new domains and subtle variations of input images. Se…
Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation
Davis Wertheimer, Omid Poursaeed, Bharath Hariharan
We aim to build image generation models that generalize to new domains from few examples. To this end, we first investigate the generalization properties of classic image generator…
Self-supervised Learning of Point Clouds via Orientation Estimation
Omid Poursaeed, Tianxing Jiang, Han Qiao +2
Point clouds provide a compact and efficient representation of 3D shapes. While deep neural networks have achieved impressive results on point cloud learning tasks, they require ma…
Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling
Omid Poursaeed, Matthew Fisher, Noam Aigerman +1
We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i.e., emb…
Fine-grained Synthesis of Unrestricted Adversarial Examples
Omid Poursaeed, Tianxing Jiang, Yordanos Goshu +3
We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation. Unlike existing unrestricted attacks that typ…