Search Results for author: Eric Tzeng

Found 18 papers, 7 papers with code

Pyramid Mini-Batching for Optimal Transport

no code implementations29 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.

Domain Adaptation

Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck

no code implementations1 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.

Image Generation

Toward Transformer-Based Object Detection

no code implementations17 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.

Natural Language Processing object-detection +1

Revisiting Few-shot Activity Detection with Class Similarity Control

no code implementations31 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.

Action Detection Activity Detection +1

Semantic Bottleneck Scene Generation

2 code implementations26 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.

Conditional Image Generation Image-to-Image Translation +1

Unsupervised Domain Adaptation through Self-Supervision

3 code implementations26 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.

Unsupervised Domain Adaptation

Learning a Unified Embedding for Visual Search at Pinterest

no code implementations5 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.

Metric Learning Recommendation Systems

SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

no code implementations3 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.

Domain Adaptation Semantic Segmentation

Adversarial Discriminative Domain Adaptation

17 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.

General Classification Unsupervised Domain Adaptation +1

Visual Discovery at Pinterest

no code implementations15 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).

object-detection Object Detection

Adapting Deep Visuomotor Representations with Weak Pairwise Constraints

no code implementations23 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.

Domain Adaptation

Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest

no code implementations12 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.

Collaborative Filtering

Simultaneous Deep Transfer Across Domains and Tasks

no code implementations 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.

Domain Adaptation

Deep Domain Confusion: Maximizing for Domain Invariance

6 code implementations10 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.

Domain Adaptation Model Selection

One-Shot Adaptation of Supervised Deep Convolutional Models

no code implementations21 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?

Domain Adaptation Image Classification

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

8 code implementations6 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.

Domain Adaptation Object Recognition +2

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