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Paying More Attention to Saliency

https://doi.org/10.1145/3177745
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot
Microsoft COCO: Common Objects in Context
inferredcites →https://doi.org/10.1007/978-3-319-46454-1_49openalex-snapshot
Where Should Saliency Models Look Next?
inferredcites →https://doi.org/10.1007/978-3-642-15561-1_2openalex-snapshot
Every Picture Tells a Story: Generating Sentences from Images
inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-snapshot
The Pascal Visual Object Classes (VOC) Challenge
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/j.neucom.2017.03.018openalex-snapshot
Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features
inferredcites →https://doi.org/10.1080/135062800394667openalex-snapshot
The Dynamic Representation of Scenes
inferredcites →https://doi.org/10.1109/34.730558openalex-snapshot
A model of saliency-based visual attention for rapid scene analysis
inferredcites →https://doi.org/10.1109/cvpr.2012.6247706openalex-snapshot
Boosting bottom-up and top-down visual features for saliency estimation
inferredcites →https://doi.org/10.1109/cvpr.2014.119openalex-snapshot
The Role of Context for Object Detection and Semantic Segmentation in the Wild
inferredcites →https://doi.org/10.1109/cvpr.2014.358openalex-snapshot
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7298633openalex-snapshot
Predicting eye fixations using convolutional neural networks
inferredcites →https://doi.org/10.1109/cvpr.2015.7298710openalex-snapshot
SALICON: Saliency in Context
inferredcites →https://doi.org/10.1109/cvpr.2015.7298932openalex-snapshot
Deep visual-semantic alignments for generating image descriptions
inferredcites →https://doi.org/10.1109/cvpr.2015.7298935openalex-snapshot
Show and tell: A neural image caption generator
inferredcites →https://doi.org/10.1109/cvpr.2015.7299087openalex-snapshot
CIDEr: Consensus-based image description evaluation
inferredcites →https://doi.org/10.1109/cvpr.2016.350openalex-snapshot
The Cityscapes Dataset for Semantic Urban Scene Understanding
inferredcites →https://doi.org/10.1109/cvpr.2016.494openalex-snapshot
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
inferredcites →https://doi.org/10.1109/cvpr.2016.503openalex-snapshot
Image Captioning with Semantic Attention
inferredcites →https://doi.org/10.1109/cvpr.2016.620openalex-snapshot
End-to-End Saliency Mapping via Probability Distribution Prediction
inferredcites →https://doi.org/10.1109/cvpr.2016.623openalex-snapshot
Saliency Unified: A Deep Architecture for simultaneous Eye Fixation Prediction and Salient Object Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.71openalex-snapshot
Shallow and Deep Convolutional Networks for Saliency Prediction
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.334openalex-snapshot
Top-Down Visual Saliency Guided by Captions
inferredcites →https://doi.org/10.1109/cvpr.2017.339openalex-snapshot
Hierarchical Boundary-Aware Neural Encoder for Video Captioning
inferredcites →https://doi.org/10.1109/cvpr.2017.356openalex-snapshot
A Hierarchical Approach for Generating Descriptive Image Paragraphs
inferredcites →https://doi.org/10.1109/cvpr.2017.715openalex-snapshot
Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing
inferredcites →https://doi.org/10.1109/iccv.2009.5459462openalex-snapshot
Learning to predict where humans look
inferredcites →https://doi.org/10.1109/iccv.2013.26openalex-snapshot
Saliency Detection: A Boolean Map Approach
inferredcites →https://doi.org/10.1109/iccv.2015.38openalex-snapshot
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks
inferredcites →https://doi.org/10.1109/iccv.2017.272openalex-snapshot
Paying Attention to Descriptions Generated by Image Captioning Models
inferredcites →https://doi.org/10.1109/iccv.2017.445openalex-snapshot
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
inferredcites →https://doi.org/10.1109/icpr.2016.7900174openalex-snapshot
A deep multi-level network for saliency prediction
inferredcites →https://doi.org/10.1109/jproc.2010.2050411openalex-snapshot
I2T: Image Parsing to Text Description
inferredcites →https://doi.org/10.1109/tpami.2012.162openalex-snapshot
BabyTalk: Understanding and Generating Simple Image Descriptions
inferredcites →https://doi.org/10.1111/1467-9280.00255openalex-snapshot
What the Eyes Say About Speaking
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1162/tacl_a_00166openalex-snapshot
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
inferredcites →https://doi.org/10.1162/tacl_a_00177openalex-snapshot
Grounded Compositional Semantics for Finding and Describing Images with Sentences
inferredcites →https://doi.org/10.1613/jair.3994openalex-snapshot
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics
inferredcites →https://doi.org/10.3115/1073083.1073135openalex-snapshot
BLEU
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1411.1045openalex-snapshot
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
inferredcites →https://doi.org/10.48550/arxiv.1502.03044openalex-snapshot
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
inferredcites →https://doi.org/10.48550/arxiv.1610.01563openalex-snapshot
DeepGaze II: Reading fixations from deep features trained on object recognition
inferredcites →https://doi.org/10.48550/arxiv.1701.01081openalex-snapshot
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
inferredcites →https://doi.org/10.5555/2566972.2566993openalex-snapshot
Framing image description as a ranking task
inferredcites →https://doi.org/10.7551/mitpress/7503.003.0073openalex-snapshot
Graph-Based Visual Saliency

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Towards Cycle-Consistent Models for Text and Image Retrieval
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GlosysIC Framework: Transformer for Image Captioning with Sequential Attention
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Pixels of Faith: Exploiting Visual Saliency to Detect Religious Image Manipulation
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Human Eye Fixations Prediction for Visual Attention Using CNN - A Survey
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Improving automated latent fingerprint detection and segmentation using deep convolutional neural network
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BENet: bi-directional enhanced network for image captioning
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FGO-Net: Feature and Gaussian Optimization Network for visual saliency prediction
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DSFNet: dynamic selection-fusion networks for video salient object detection
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aiTPR: Attribute Interaction-Tensor Product Representation for Image Caption
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Complementary Shifted Transformer for Image Captioning
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Inferring Attention Shifts for Salient Instance Ranking
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Weakly Supervised Salient Object Detection with Text Supervision
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Saliency detection in human crowd images of different density levels using attention mechanism
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The Visual Saliency Transformer Goes Temporal: TempVST for Video Saliency Prediction
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Towards Efficient Visual Attention Prediction for 360 Degree Videos
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Enhanced Text-Guided Attention Model for Image Captioning
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Saliency detection from subitizing processing: First Approximation
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How Much Time Do You Have? Modeling Multi-Duration Saliency
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Visual Saliency Prediction Based on Deep Learning
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