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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network ... Ye, Linwei; Rochan, Mrigank; Liu, Zhi. - : arXiv, 2021 BASE Show details 2 Dual Convolutional LSTM Network for Referring Image Segmentation ... Ye, Linwei; Liu, Zhi; Wang, Yang. - : arXiv, 2020 BASE Show details 3 Natural language guided image and video understanding Ye, Linwei. - 2020 BASE Show details 4 Cross-Modal Self-Attention Network for Referring Image Segmentation ... Ye, Linwei; Rochan, Mrigank; Liu, Zhi; Wang, Yang. - : arXiv, 2019 Abstract: We consider the problem of referring image segmentation. Given an input image and a natural language expression, the goal is to segment the object referred by the language expression in the image. Existing works in this area treat the language expression and the input image separately in their representations. They do not sufficiently capture long-range correlations between these two modalities. In this paper, we propose a cross-modal self-attention (CMSA) module that effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the input image. In addition, we propose a gated multi-level fusion module to selectively integrate self-attentive cross-modal features corresponding to different levels in the image. This module controls the information flow of features at different levels. We validate the proposed approach on four evaluation datasets. Our proposed approach consistently ... : Accepted to CVPR2019 ... Keyword: Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences URL: https://arxiv.org/abs/1904.04745 https://dx.doi.org/10.48550/arxiv.1904.04745 BASE Hide details

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