Search Results for author:
Found 31 papers, 17 papers with code
COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
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 mapping.
Open Set Domain Adaptation by Backpropagation
Almost all of them are proposed for a closed-set scenario, where the source and the target domain completely share the class of their samples.
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries.
Ranked #3 on
Domain Adaptation
on HMDBfull-to-UCF
Image Classification
Multi-Source Unsupervised Domain Adaptation
+2
Strong-Weak Distribution Alignment for Adaptive Object Detection
This motivates us to propose a novel method for detector adaptation based on strong local alignment and weak global alignment.
Ranked #2 on
Unsupervised Domain Adaptation
on SIM10K to BDD100K
Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains.
Semi-supervised Domain Adaptation via Minimax Entropy
Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision.
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image.
Ranked #1 on
Zero-shot Image Retrieval
on ImageNet-R
Universal Domain Adaptation through Self Supervision
While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori.
Melody Generation for Pop Music via Word Representation of Musical Properties
Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians.
Sound Multimedia Audio and Speech Processing
OVANet: One-vs-All Network for Universal Domain Adaptation
In this paper, we propose a method to learn the threshold using source samples and to adapt it to the target domain.
Ranked #6 on
Universal Domain Adaptation
on DomainNet
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers
OpenMatch achieves state-of-the-art performance on three datasets, and even outperforms a fully supervised model in detecting outliers unseen in unlabeled data on CIFAR10.
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization
\ours achieves state-of-the-art performance on three datasets, and even outperforms a fully supervised model in detecting outliers unseen in unlabeled data on CIFAR10.
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i. e., the same domain.
Asymmetric Tri-training for Unsupervised Domain Adaptation
Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks.
Ranked #5 on
Sentiment Analysis
on Multi-Domain Sentiment Dataset
Multichannel Semantic Segmentation with Unsupervised Domain Adaptation
The other is a multitask learning approach that uses depth images as outputs.
ERM++: An Improved Baseline for Domain Generalization
We also explore the relationship between DG performance and similarity to pre-training data, and find that similarity to pre-training data distributions is an important driver of performance, but that ERM++ with stronger initializations can deliver strong performance even on dissimilar datasets. Code is released at https://github. com/piotr-teterwak/erm_plusplus.
Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection
We propose to use Relative Gradient Norm (RGN) as a way to measure the vulnerability of a backbone to feature distortion, and show that high RGN is indeed correlated with lower OOD performance.
Adversarial Dropout Regularization
However, a drawback of this approach is that the critic simply labels the generated features as in-domain or not, without considering the boundaries between classes.
Ranked #2 on
Synthetic-to-Real Translation
on Syn2Real-C
Spatio-temporal Person Retrieval via Natural Language Queries
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 bounding boxes) which encloses the person described by the query.
DualNet: Domain-Invariant Network for Visual Question Answering
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 linguistic context of the question, in order to generate the accurate answers.
DeMIAN: Deep Modality Invariant Adversarial Network
To obtain the common representations under such a situation, we propose to make the distributions over different modalities similar in the learned representations, namely modality-invariant representations.
Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation
In this paper, we present a new large-scale benchmark called Syn2Real, which consists of a synthetic domain rendered from 3D object models and two real-image domains containing the same object categories.
TWINs: Two Weighted Inconsistency-reduced Networks for Partial Domain Adaptation
We utilize two classification networks to estimate the ratio of the target samples in each class with which a classification loss is weighted to adapt the classes present in the target domain.
MULE: Multimodal Universal Language Embedding
In this paper, we present a modular approach which can easily be incorporated into existing vision-language methods in order to support many languages.
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels
We show that when labeled source examples are limited, existing methods often fail to learn discriminative features applicable for both source and target domains.
Self-supervised Visual Attribute Learning for Fashion Compatibility
Our approach consists of three self-supervised tasks designed to capture different concepts that are neglected in prior work that we can select from depending on the needs of our downstream tasks.
CDS: Cross-Domain Self-Supervised Pre-Training
We present a two-stage pre-training approach that improves the generalization ability of standard single-domain pre-training.
Pyramid Mini-Batching for Optimal Transport
Optimal transport theory provides a useful tool to measure the differences between two distributions.
Learning to Detect Every Thing in an Open World
LDET leads to significant improvements on many datasets in the open-world instance segmentation task, outperforming baselines on cross-category generalization on COCO, as well as cross-dataset evaluation on UVO and Cityscapes.
Prefix Conditioning Unifies Language and Label Supervision
In experiments, we show that this simple technique improves the performance in zero-shot image recognition accuracy and robustness to the image-level distribution shift.
Unsupervised LLM Adaptation for Question Answering
In this task, we leverage a pre-trained LLM, a publicly available QA dataset (source data), and unlabeled documents from the target domain.
