Multi-source Domain Generalization (DG) measures a classifier's ability ...
Building object detectors that are robust to domain shifts is critical f...
In Composed Image Retrieval (CIR), a user combines a query image with te...
Vision-language contrastive learning suggests a new learning paradigm by...
Many open-world applications require the detection of novel objects, yet...
Unsupervised domain adaptation (UDA) methods can dramatically improve
ge...
Progress in machine learning is typically measured by training and testi...
Semi-supervised learning (SSL) is an effective means to leverage unlabel...
Universal Domain Adaptation (UNDA) aims to handle both domain-shift and
...
Many self-supervised learning (SSL) methods have been successful in lear...
Unsupervised image-to-image translation intends to learn a mapping of an...
Existing unsupervised domain adaptation methods aim to transfer knowledg...
Unsupervised domain adaptation methods traditionally assume that all sou...
Existing vision-language methods typically support two languages at a ti...
Contemporary domain adaptation methods are very effective at aligning fe...
The task of unsupervised domain adaptation is proposed to transfer the
k...
We propose an approach for unsupervised adaptation of object detectors f...
Most contemporary robots have depth sensors, and research on semantic
se...
Unsupervised transfer of object recognition models from synthetic to rea...
Numerous algorithms have been proposed for transferring knowledge from a...
In this work, we present a method for unsupervised domain adaptation (UD...
We present a method for transferring neural representations from label-r...
Automatic melody generation for pop music has been a long-time aspiratio...
Deep-layered models trained on a large number of labeled samples boost t...
Obtaining common representations from different modalities is important ...
Visual question answering (VQA) task not only bridges the gap between im...