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Predicting memorability of images using attention-driven spatial pooling and image semantics
2015
Image and Vision Computing
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In particular, we present an attention-driven spatial pooling strategy and show that considering image features from the salient parts of images improves the results of the previous models. ...
Our goal in this article is to explore the role of visual attention and image semantics in understanding image memorability. ...
Acknowledgments This work was supported by a grant from The Scientific and Technological Research Council of Turkey (TUBITAK) -Career Development Award 112E146. ...
doi:10.1016/j.imavis.2015.07.001
fatcat:qdbb4yfplberjbcchcqlukh22m
Visual Attention-Driven Spatial Pooling for Image Memorability
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
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In particular, we present an attention-driven spatial pooling strategy for image memorability and show that the regions estimated by bottom-up and object-level saliency maps are more effective in predicting ...
In daily life, humans demonstrate astounding ability to remember images they see on magazines, commercials, TV, the web and so on, but automatic prediction of intrinsic memorability of images using computer ...
Acknowledgments This research was supported in part by The Scientic and Technological Research Council of Turkey (TUBITAK), Career Development Award 112E146. ...
doi:10.1109/cvprw.2013.142
dblp:conf/cvpr/CelikkaleEE13
fatcat:4y6vzrjk4bemhdxghickd5sw5u
Memorability of Image Regions
2012
Neural Information Processing Systems
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Recent works have shown that image memorability is an intrinsic property of an image that can be reliably estimated using state-of-the-art image features and machine learning algorithms. ...
In this work, we propose a probabilistic framework that models how and which local regions from an image may be forgotten using a data-driven approach that combines local and global images features. ...
Acknowledgements We thank Phillip Isola and the reviewers for helpful discussions. ...
dblp:conf/nips/KhoslaXTO12
fatcat:3ih73tx5m5ew7giew7x4hqm75e
Dublin's Participation in the Predicting Media Memorability Task at MediaEval 2018
2018
MediaEval Benchmarking Initiative for Multimedia Evaluation
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This paper outlines 6 approaches taken to computing video memorability, for the MediaEval Predicting Media Memorability Task. ...
The approaches are based on video features, an end-to-end approach, saliency, aesthetics, neural feedback, and an ensemble of all approaches. ...
Using Video and Image Saliency Visual saliency models generate a probability map highlighting image regions that most attract human attention. ...
dblp:conf/mediaeval/SmeatonCDGHHMMW18
fatcat:jfrsfcxlczf6nguqhthkm4aw6e
Memorability: An image-computable measure of information utility
[article]
2021
arXiv
pre-print
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In this chapter, we zoom into memorability with a computational lens, detailing the state-of-the-art algorithms that accurately predict image memorability relative to human behavioral data, using image ...
Observer independence is what makes memorability an image-computable measure of information, and eligible for automatic prediction. ...
They report that spatially pooling and weighting image features (e.g., GIST, SIFT, HOG) by the VMS not only yields better SVR predictions than without doing spatial pooling, but also outperforms spatial ...
arXiv:2104.00805v1
fatcat:ckncvcnyavedxkbxyfmu6gon5a
Shared memories driven by the intrinsic memorability of items
[article]
2021
arXiv
pre-print
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Certain items -- including certain faces, words, images, and movements -- are intrinsically memorable or forgettable across observers, regardless of individual differences. ...
In this chapter, I will discuss our current state-of-the-art understanding of memorability for visual information, and what these findings imply about how we perceive and remember visual events. ...
Image color is not highly predictive of image memorability (Isola et al., 2011) , nor is spatial frequency . ...
arXiv:2104.06937v1
fatcat:infxy6tjfjbjtjbdr7dturz6oy
Predicting Image Memorability by Multi-view Adaptive Regression
2015
Proceedings of the 23rd ACM international conference on Multimedia - MM '15
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Experimental results on the MIT benchmark dataset show the superiority of the proposed model compared with existing image memorability prediction methods. ...
This phenomenon is caused by the intrinsic memorability of images revealed by recent studies [5, 6] . ...
[2] introduce an attention-driven spatial pooling strategy for feature encoding and adopt SVR for prediction. ...
doi:10.1145/2733373.2806303
dblp:conf/mm/PengLLLXH15
fatcat:dcqy34c5t5aglcvzyeiewk5ave
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling
[article]
2016
arXiv
pre-print
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and long-range spatial dependencies in an image along the vertical direction. ...
Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. ...
In future, we will explore how to extend the memorized layers with an attention mechanism, and refine the performance of our model in boundary labeling. Fig. 6 . ...
arXiv:1604.05000v3
fatcat:hs33ywcpbraxhieubxv3odt23i
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling
[chapter]
2016
Lecture Notes in Computer Science
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and longrange spatial dependencies in an image along the vertical direction. ...
Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. ...
In future, we will explore how to extend the memorized layers with an attention mechanism, and refine the performance of our model in boundary labeling. Fig. 6 . ...
doi:10.1007/978-3-319-46475-6_34
fatcat:lofxpg2obfenrjufd55vwcrdy4
Simultaneous Mapping and Target Driven Navigation
[article]
2019
arXiv
pre-print
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We demonstrate that the use of semantic information improves localization accuracy and the ability of storing spatial semantic map aids the target driven navigation policy. ...
The semantic and appearance stored in 2.5D map is distilled from RGB images, semantic segmentation and outputs of object detectors by convolutional neural networks. ...
The mapping component leverages the outputs of object detection and semantic segmentation to construct a spatial representation of a scene which contains some semantic information. ...
arXiv:1911.07980v1
fatcat:n2onyhoebzapri7bx7qjjwzo5y
Learning deep representations for semantic image parsing: a comprehensive overview
2018
Frontiers of Computer Science
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2018 pre-print arXiv:1810.04377v1 fatcat:67xjrrltbzekphdnq23xyrhmze
Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the ...
In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation ...
Works beyond segmentation not only semantically segment images but also predict richer and finer results, such as the structures and relations of objects and the spatial layout. ...
doi:10.1007/s11704-018-7195-8
fatcat:p5hvfwhl5rbork5vf4rpnx3h6u
Relation Network for Multilabel Aerial Image Classification
2020
IEEE Transactions on Geoscience and Remote Sensing
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The label relational inference module finally predicts label existences using label relations reasoned from outputs of the previous module. ...
Multilabel classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. ...
However, spatial contextual semantics are not taken into account in this way. To address such an issue, here, we make use of 1 × 1 convolution instead of an MLP to explore spatial information. ...
doi:10.1109/tgrs.2019.2963364
fatcat:lpk2hmjhc5doplnxuyvyyen3fy
ResMem-Net: memory based deep CNN for image memorability estimation
2021
PeerJ Computer Science
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But due to the introduction of Deep Learning and the large availability of data and GPUs, great strides have been made in predicting the memorability of an image. ...
We have also used the heatmaps and results to analyze and answer one of the most important questions in image memorability: "What makes an image memorable?". ...
The idea of using data-driven strategies to predict image memorability was first introduced by Isola et al. (2011a) . ...
doi:10.7717/peerj-cs.767
pmid:34825056
pmcid:PMC8594589
fatcat:xptm6a3fj5aevknxc547qcco4i
Coastal Land Cover Classification of High-Resolution Remote Sensing Images Using Attention-Driven Context Encoding Network
2020
Sensors
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Therefore, this paper proposes a novel attention-driven context encoding network to solve these problems. ...
sensing images. ...
funding this study; Fengliang Fan for providing the datasets and hardware support used in this study. ...
doi:10.3390/s20247032
pmid:33302547
pmcid:PMC7763023
fatcat:p7wpwg4mxna4dj6gq7bckbauqu
Relation Network for Multi-label Aerial Image Classification
[article]
2020
arXiv
pre-print
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The label relational inference module finally predicts label existences using label relations reasoned from outputs of the previous module. ...
Multi-label classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. ...
However, spatial contextual semantics are not taken into account in this way. To address such issue, here, we make use of 1 × 1 convolution instead of an MLP to explore spatial information. ...
arXiv:1907.07274v3
fatcat:2lqaw6qxp5cavel44qytlz7ali
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