Search Results for author:
Found 7 papers, 3 papers with code
Shatter and Gather: Learning Referring Image Segmentation with Text Supervision
Referring image segmentation, the task of segmenting any arbitrary entities described in free-form texts, opens up a variety of vision applications.
Learning to Detect Semantic Boundaries with Image-level Class Labels
This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision.
Improving Cross-Modal Retrieval with Set of Diverse Embeddings
It seeks to encode a sample into a set of different embedding vectors that capture different semantics of the sample.
ReSTR: Convolution-free Referring Image Segmentation Using Transformers
Most of existing methods for this task rely heavily on convolutional neural networks, which however have trouble capturing long-range dependencies between entities in the language expression and are not flexible enough for modeling interactions between the two different modalities.
Ranked #12 on
Referring Expression Segmentation
on RefCoCo val
Style Neophile: Constantly Seeking Novel Styles for Domain Generalization
Existing methods in this direction suppose that a domain can be characterized by styles of its images, and train a network using style-augmented data so that the network is not biased to particular style distributions.
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation
We also present a method which injects styles of the web-crawled images into training images on-the-fly during training, which enables the network to experience images of diverse styles with reliable labels for effective training.
URIE: Universal Image Enhancement for Visual Recognition in the Wild
Despite the great advances in visual recognition, it has been witnessed that recognition models trained on clean images of common datasets are not robust against distorted images in the real world.
