Top-down Visual Saliency Guided by Captions

Abstract

Neural image/video captioning models can generate accurate descriptions, but their internal process of mapping regions to words is a black box and therefore difficult to explain. Top-down neural saliency methods can find important regions given a high-level semantic task such as object classification, but cannot use a natural language sentence as the top-down input for the task. In this paper, we propose Caption-Guided Visual Saliency to expose the region-to-word mapping in modern encoder-decoder networks and demonstrate that it is learned implicitly from caption training data, without any pixel-level annotations. Our approach can produce spatial or spatio-temporal heatmaps for both predicted captions, and for arbitrary query sentences. It recovers saliency without the overhead of introducing explicit attention layers, and can be used to analyze a variety of existing model architectures and improve their design. Evaluation on large-scale video and image datasets demonstrates that our approach achieves comparable captioning performance with existing methods while providing more accurate saliency heatmaps.

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Overview


Code

The code to prepare data and train the model can be found in:
https://github.com/VisionLearningGroup/caption-guided-saliency


Reference

If you find this useful in your work please consider citing:

  
          @inproceedings{Ramanishka2017cvpr,
          title = {Top-down Visual Saliency Guided by Captions},
          author = {Vasili Ramanishka and Abir Das and Jianming Zhang and Kate Saenko},
          booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
          year = {2017}
          }