Progressive Attention Networks for Visual Attribute Prediction

By Paul Hongsuck Seo, Zhe L. Lin, Scott D. Cohen, Xiaohui Shen, Bohyung Han. 2016

41 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sean Welleck
2. Paul Hongsuck Seo
3. Zheng Zhang
4. Bohyung Han
5. Kyunghyun Cho
6. Zhe Gan
7. Leonid Sigal
8. Yoshua Bengio

Citations in the corpus

• Visual Reference Resolution using Attention Memory for Visual Dialog (2017, methodology): “Beyond VQA, attention models are used to find visual groundings of linguistic expressions in a variety of other multi-modal tasks, such as image captioning [3, 4], VQA in videos [22], and visual attributes prediction [28].”
• Saliency-based Sequential Image Attention with Multiset Prediction (2017, background): “Progressive Attention Networks [36] learn attention filters placed at each layer of a CNN to progressively focus on an arbitrary subset of the input, while Residual Attention Networks [41] learn feature-specific filters.”