no code implementations • 20 Jan 2024 • Yinan Zhang, Eric Tzeng, Yilun Du, Dmitry Kislyuk
Text-to-image diffusion models are a class of deep generative models that have demonstrated an impressive capacity for high-quality image generation.
no code implementations • 29 Sep 2021 • Devin Guillory, Kuniaki Saito, Eric Tzeng, Yannik Pitcan, Kate Saenko, Trevor Darrell
Optimal transport theory provides a useful tool to measure the differences between two distributions.
no code implementations • 1 Jan 2021 • Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottleneck GAN model for unconditional synthesis of complex scenes.
no code implementations • 17 Dec 2020 • Josh Beal, Eric Kim, Eric Tzeng, Dong Huk Park, Andrew Zhai, Dmitry Kislyuk
The Vision Transformer was the first major attempt to apply a pure transformer model directly to images as input, demonstrating that as compared to convolutional networks, transformer-based architectures can achieve competitive results on benchmark classification tasks.
no code implementations • 31 Mar 2020 • Huijuan Xu, Ximeng Sun, Eric Tzeng, Abir Das, Kate Saenko, Trevor Darrell
In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection based on proposal regression which detects the start and end time of the activities in untrimmed videos.
2 code implementations • 26 Nov 2019 • Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic
For the former, we use an unconditional progressive segmentation generation network that captures the distribution of realistic semantic scene layouts.
Ranked #1 on Image Generation on Cityscapes-5K 256x512
3 code implementations • 26 Sep 2019 • Yu Sun, Eric Tzeng, Trevor Darrell, Alexei A. Efros
This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data.
no code implementations • 5 Aug 2019 • Andrew Zhai, Hao-Yu Wu, Eric Tzeng, Dong Huk Park, Charles Rosenberg
The solution we present not only allows us to train for multiple application objectives in a single deep neural network architecture, but takes advantage of correlated information in the combination of all training data from each application to generate a unified embedding that outperforms all specialized embeddings previously deployed for each product.
no code implementations • 3 Dec 2018 • Eric Tzeng, Kaylee Burns, Kate Saenko, Trevor Darrell
Without dense labels, as is the case when only detection labels are available in the source, transformations are learned using CycleGAN alignment.
3 code implementations • ICML 2018 • Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell
Domain adaptation is critical for success in new, unseen environments.
20 code implementations • CVPR 2017 • Eric Tzeng, Judy Hoffman, Kate Saenko, Trevor Darrell
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.
no code implementations • 15 Feb 2017 • Andrew Zhai, Dmitry Kislyuk, Yushi Jing, Michael Feng, Eric Tzeng, Jeff Donahue, Yue Li Du, Trevor Darrell
Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016) and Lens (2017).
no code implementations • 23 Nov 2015 • Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel, Sergey Levine, Kate Saenko, Trevor Darrell
We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains.
no code implementations • 12 Nov 2015 • Dmitry Kislyuk, Yuchen Liu, David Liu, Eric Tzeng, Yushi Jing
This paper presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking.
1 code implementation • ICCV 2015 • Eric Tzeng, Judy Hoffman, Trevor Darrell, Kate Saenko
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias.
7 code implementations • 10 Dec 2014 • Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, Trevor Darrell
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark.
Ranked #6 on Domain Adaptation on Office-Caltech
1 code implementation • NeurIPS 2014 • Judy Hoffman, Sergio Guadarrama, Eric Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko
A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories.
no code implementations • 21 Dec 2013 • Judy Hoffman, Eric Tzeng, Jeff Donahue, Yangqing Jia, Kate Saenko, Trevor Darrell
In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be?
8 code implementations • 6 Oct 2013 • Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.