451 citations · 1.1k across the 28 of their papers we have counts for
23 papers · 1 filter
Is Large-Scale Pretraining the Secret to Good Domain Generalization?
Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis +2
Multi-Source Domain Generalization (DG) is the task of training on multiple source domains and achieving high classification performance on unseen target domains. Recent methods co…
Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection
Kuniaki Saito, Donghyun Kim, Piotr Teterwak +2
Building object detectors that are robust to domain shifts is critical for real-world applications. Prior approaches fine-tune a pre-trained backbone and risk overfitting it to in-…
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
Kuniaki Saito, Kihyuk Sohn, Xiang Zhang +4
In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using label…
Prefix Conditioning Unifies Language and Label Supervision
Kuniaki Saito, Kihyuk Sohn, Xiang Zhang +4
Image-classification datasets have been used to pretrain image recognition models. Recently, web-scale image-caption datasets have emerged as a source of powerful pretraining alter…
Learning to Detect Every Thing in an Open World
Kuniaki Saito, Ping Hu, Trevor Darrell +1
Many open-world applications require the detection of novel objects, yet state-of-the-art object detection and instance segmentation networks do not excel at this task. The key iss…
Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
Kuniaki Saito, Donghyun Kim, Piotr Teterwak +3
Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains. However, optimal hyper-parameter selection is critical to achievin…
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers
Kuniaki Saito, Donghyun Kim, Kate Saenko
Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model's performance. Typical SSL methods like FixMatch assume that labeled and unlabele…
OVANet: One-vs-All Network for Universal Domain Adaptation
Kuniaki Saito, Kate Saenko
Universal Domain Adaptation (UNDA) aims to handle both domain-shift and category-shift between two datasets, where the main challenge is to transfer knowledge while rejecting unkno…
Self-supervised Visual Attribute Learning for Fashion Compatibility
Donghyun Kim, Kuniaki Saito, Samarth Mishra +3
Many self-supervised learning (SSL) methods have been successful in learning semantically meaningful visual representations by solving pretext tasks. However, prior work in SSL foc…
COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
Kuniaki Saito, Kate Saenko, Ming-Yu Liu
Unsupervised image-to-image translation intends to learn a mapping of an image in a given domain to an analogous image in a different domain, without explicit supervision of the ma…
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels
Donghyun Kim, Kuniaki Saito, Tae-Hyun Oh +3
Existing unsupervised domain adaptation methods aim to transfer knowledge from a label-rich source domain to an unlabeled target domain. However, obtaining labels for some source d…
Universal Domain Adaptation through Self Supervision
Kuniaki Saito, Donghyun Kim, Stan Sclaroff +1
Unsupervised domain adaptation methods traditionally assume that all source categories are present in the target domain. In practice, little may be known about the category overlap…
MULE: Multimodal Universal Language Embedding
Donghyun Kim, Kuniaki Saito, Kate Saenko +2
Existing vision-language methods typically support two languages at a time at most. In this paper, we present a modular approach which can easily be incorporated into existing visi…
Semi-supervised Domain Adaptation via Minimax Entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff +2
Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision. However, we show that these…
Multichannel Semantic Segmentation with Unsupervised Domain Adaptation
Kohei Watanabe, Kuniaki Saito, Yoshitaka Ushiku +1
Most contemporary robots have depth sensors, and research on semantic segmentation with RGBD images has shown that depth images boost the accuracy of segmentation. Since it is time…
Strong-Weak Distribution Alignment for Adaptive Object Detection
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada +1
We propose an approach for unsupervised adaptation of object detectors from label-rich to label-poor domains which can significantly reduce annotation costs associated with detecti…
Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation
Xingchao Peng, Ben Usman, Kuniaki Saito +3
Unsupervised transfer of object recognition models from synthetic to real data is an important problem with many potential applications. The challenge is how to "adapt" a model tra…
Open Set Domain Adaptation by Backpropagation
Kuniaki Saito, Shohei Yamamoto, Yoshitaka Ushiku +1
Numerous algorithms have been proposed for transferring knowledge from a label-rich domain (source) to a label-scarce domain (target). Almost all of them are proposed for a closed-…
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku +1
In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a sou…
Adversarial Dropout Regularization
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada +1
We present a method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to a…
Spatio-temporal Person Retrieval via Natural Language Queries
Masataka Yamaguchi, Kuniaki Saito, Yoshitaka Ushiku +1
In this paper, we address the problem of spatio-temporal person retrieval from multiple videos using a natural language query, in which we output a tube (i.e., a sequence of boundi…
Asymmetric Tri-training for Unsupervised Domain Adaptation
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada
Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks. It is important to apply such models to different domains because collecting many…
DualNet: Domain-Invariant Network for Visual Question Answering
Kuniaki Saito, Andrew Shin, Yoshitaka Ushiku +1
Visual question answering (VQA) task not only bridges the gap between images and language, but also requires that specific contents within the image are understood as indicated by…