Multi-level Multimodal Common Semantic Space for Image-Phrase Grounding
arXiv:1811.11683
Abstract
We address the problem of phrase grounding by lear ing a multi-level common semantic space shared by the textual and visual modalities. We exploit multiple levels of feature maps of a Deep Convolutional Neural Network, as well as contextualized word and sentence embeddings extracted from a character-based language model. Following dedicated non-linear mappings for visual features at each level, word, and sentence embeddings, we obtain multiple instantiations of our common semantic space in which comparisons between any target text and the visual content is performed with cosine similarity. We guide the model by a multi-level multimodal attention mechanism which outputs attended visual features at each level. The best level is chosen to be compared with text content for maximizing the pertinence scores of image-sentence pairs of the ground truth. Experiments conducted on three publicly available datasets show significant performance gains (20%-60% relative) over the state-of-the-art in phrase localization and set a new performance record on those datasets. We provide a detailed ablation study to show the contribution of each element of our approach and release our code on GitHub.
arXiv commentAccepted in CVPR 2019
References in corpus (9)
- Adam: A Method for Stochastic Optimization
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Understanding Neural Networks Through Deep Visualization
- VQA: Visual Question Answering
- Exploring the Limits of Language Modeling
- Dynamic Memory Networks for Visual and Textual Question Answering
- Progressive Neural Architecture Search
- Attention Correctness in Neural Image Captioning
- Weakly-supervised Visual Grounding of Phrases with Linguistic Structures