image region
Visual Question Answering with Question Representation Update (QRU)
Neural Information Processing Systems http://nips.cc/
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
Text-to-image generation has recently witnessed remarkable achievements.
Adaptively Aligned Image Captioning via Adaptive Attention Time
Lun Huang, Wenmin Wang, Yaxian Xia, Jie Chen
AATallowstheframeworktolearn howmany attention steps to take to output a caption word at each decoding step. With AAT, an image region can be mapped to an arbitrary number of caption words while a caption word can also attend to an arbitrary number of image regions. AAT is deterministic and differentiable, and doesn't introduce any noise to the parameter gradients.
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee
Neural Information Processing Systems http://nips.cc/
Unsupervised Attention-guided Image-to-Image Translation
Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim
Neural Information Processing Systems http://nips.cc/
LookattheVariance! EfficientBlack-box ExplanationswithSobol-basedSensitivityAnalysis
Our code is freely available:github.com/fel-thomas/
Hard-AttentionforScalableImageClassification
In image classification, deep neural networks (DNNs) are typically designed and optimized for a specificinputresolution,e.g.
ProphetAttention: PredictingAttentionwithFutureAttention
However,foreachtimestepinthedecoding process, theattention based models usually use the hidden state of the current input to attend to the image regions.