Search Results for author:
Found 8 papers, 4 papers with code
CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification
Our proposed framework is built on a masked Vim autoencoder to learn a unified multi-modal representation and long-dependencies contained in 3D medical images.
BiSVP: Building Footprint Extraction via Bidirectional Serialized Vertex Prediction
Extracting building footprints from remote sensing images has been attracting extensive attention recently.
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Self-training has shown great potential in semi-supervised learning.
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast
Moreover, armed with our method, we increase the segmentation mIoU of EPS from 70. 8% to 73. 6%, achieving new state-of-the-art.
Ranked #14 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Visual Grounding with Transformers
In this paper, we propose a transformer based approach for visual grounding.
Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs
In this work, we propose a new retrofitting method called Heterogeneously Retrofitted Spectral Word Embedding.
ARM: A Confidence-Based Adversarial Reweighting Module for Coarse Semantic Segmentation
Experiments on standard datasets shows our ARM can bring consistent improvements for both coarse annotations and fine annotations.
Tackling Adversarial Examples in QA via Answer Sentence Selection
Given a paragraph of an article and a corresponding query, instead of directly feeding the whole paragraph to the single BiDAF system, a sentence that most likely contains the answer to the query is first selected, which is done via a deep neural network based on TreeLSTM (Tai et al., 2015).
