Search Results for author: Sayna Ebrahimi

Found 20 papers, 9 papers with code

Contrastive Test-Time Adaptation

no code implementations21 Apr 2022 Dian Chen, Dequan Wang, Trevor Darrell, Sayna Ebrahimi

We propose a novel way to leverage self-supervised contrastive learning to facilitate target feature learning, along with an online pseudo labeling scheme with refinement that significantly denoises pseudo labels.

Contrastive Learning Unsupervised Domain Adaptation

Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image

no code implementations ICLR 2022 Shizhan Zhu, Sayna Ebrahimi, Angjoo Kanazawa, Trevor Darrell

Existing approaches for single object reconstruction impose supervision signals based on the loss of the signed distance value from all locations in a scene, posing difficulties when extending to real-world scenarios.

Indoor Scene Reconstruction Object Reconstruction +1

Zero-Shot Reward Specification via Grounded Natural Language

no code implementations29 Sep 2021 Parsa Mahmoudieh, Sayna Ebrahimi, Deepak Pathak, Trevor Darrell

Reward signals in reinforcement learning can be expensive signals in many tasks and often require access to direct state.

reinforcement-learning

On-target Adaptation

1 code implementation2 Sep 2021 Dequan Wang, Shaoteng Liu, Sayna Ebrahimi, Evan Shelhamer, Trevor Darrell

Domain adaptation seeks to mitigate the shift between training on the \emph{source} domain and testing on the \emph{target} domain.

Domain Adaptation

Region-level Active Detector Learning

no code implementations20 Aug 2021 Michael Laielli, Giscard Biamby, Dian Chen, Ritwik Gupta, Adam Loeffler, Phat Dat Nguyen, Ross Luo, Trevor Darrell, Sayna Ebrahimi

Active learning for object detection is conventionally achieved by applying techniques developed for classification in a way that aggregates individual detections into image-level selection criteria.

Active Learning Object Detection

Predicting with Confidence on Unseen Distributions

no code implementations ICCV 2021 Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, Ludwig Schmidt

Our work connects techniques from domain adaptation and predictive uncertainty literature, and allows us to predict model accuracy on challenging unseen distributions without access to labeled data.

Domain Adaptation

Self-Supervised Pretraining Improves Self-Supervised Pretraining

1 code implementation23 Mar 2021 Colorado J. Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shanghang Zhang, Devin Guillory, Sean Metzger, Kurt Keutzer, Trevor Darrell

Through experimentation on 16 diverse vision datasets, we show HPT converges up to 80x faster, improves accuracy across tasks, and improves the robustness of the self-supervised pretraining process to changes in the image augmentation policy or amount of pretraining data.

Image Augmentation

Minimax Active Learning

no code implementations18 Dec 2020 Sayna Ebrahimi, William Gan, Dian Chen, Giscard Biamby, Kamyar Salahi, Michael Laielli, Shizhan Zhu, Trevor Darrell

Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator.

Active Learning Image Classification +1

Adversarial Continual Learning

1 code implementation ECCV 2020 Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, Marcus Rohrbach

We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks.

Continual Learning Image Classification

WiCV 2019: The Sixth Women In Computer Vision Workshop

no code implementations23 Sep 2019 Irene Amerini, Elena Balashova, Sayna Ebrahimi, Kathryn Leonard, Arsha Nagrani, Amaia Salvador

In this paper we present the Women in Computer Vision Workshop - WiCV 2019, organized in conjunction with CVPR 2019.

Variational Adversarial Active Learning

6 code implementations ICCV 2019 Samarth Sinha, Sayna Ebrahimi, Trevor Darrell

Unlike conventional active learning algorithms, our approach is task agnostic, i. e., it does not depend on the performance of the task for which we are trying to acquire labeled data.

Active Learning Image Classification +1

Compositional GAN (Extended Abstract): Learning Image-Conditional Binary Composition

no code implementations ICLR Workshop DeepGenStruct 2019 Samaneh Azadi, Deepak Pathak, Sayna Ebrahimi, Trevor Darrell

Generative Adversarial Networks (GANs) can produce images of surprising complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene.

Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning

no code implementations ICLR Workshop LLD 2019 Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata

While following the same direction, we also take artificial feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by aligned variational autoencoders, for the purpose of generating latent features to train a softmax classifier.

Few-Shot Learning Generalized Zero-Shot Learning

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

2 code implementations5 Dec 2018 Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata

Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.

Few-Shot Learning Generalized Zero-Shot Learning

Uncertainty-guided Lifelong Learning in Bayesian Networks

no code implementations27 Sep 2018 Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach

Sequentially learning of tasks arriving in a continuous stream is a complex problem and becomes more challenging when the model has a fixed capacity.

Continual Learning

Compositional GAN: Learning Image-Conditional Binary Composition

1 code implementation19 Jul 2018 Samaneh Azadi, Deepak Pathak, Sayna Ebrahimi, Trevor Darrell

Generative Adversarial Networks (GANs) can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene.

Gradient-free Policy Architecture Search and Adaptation

no code implementations16 Oct 2017 Sayna Ebrahimi, Anna Rohrbach, Trevor Darrell

We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks.

Autonomous Driving Neural Architecture Search

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