Search Results for author:
Found 7 papers, 3 papers with code
Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network
In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features.
Ranked #5 on
Referring Expression Segmentation
on J-HMDB
(Precision@0.9 metric)
Adaptive Video Highlight Detection by Learning from User History
In this paper, we propose a simple yet effective framework that learns to adapt highlight detection to a user by exploiting the user's history in the form of highlights that the user has previously created.
Cross-Modal Weighting Network for RGB-D Salient Object Detection
In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD.
Ranked #9 on
RGB-D Salient Object Detection
on NJU2K
Dual Convolutional LSTM Network for Referring Image Segmentation
Given an input image and a referring expression in the form of a natural language sentence, the goal is to segment the object of interest in the image referred by the linguistic query.
Cross-Modal Self-Attention Network for Referring Image Segmentation
This module controls the information flow of features at different levels.
Ranked #14 on
Referring Video Object Segmentation
on Refer-YouTube-VOS
(using extra training data)
Video Summarization Using Fully Convolutional Sequence Networks
This paper addresses the problem of video summarization.
Learning Semantic Segmentation with Diverse Supervision
Models based on deep convolutional neural networks (CNN) have significantly improved the performance of semantic segmentation.
