Name
Title | ||
|---|---|---|
Referring Expression Comprehension with Semantic Visual Relationship and Word Mapping |
Abstract | ||
|---|---|---|
Referring expression comprehension, which locates the object instance described by a natural language expression, gains increasing interests in recent years. This paper aims at improving the task from two aspects: visual feature extraction and language features extraction. For visual feature extraction, we observe that most of the previous methods utilize only relative spatial information to model the visual relationship between object pairs while discarding rich semantic relationship between objects. This makes the visual-language matching difficult when the language expression contains semantic relationship to discriminate the referred object from other objects in the image. In this work, we propose a Semantic Visual Relationship Module (SVRM) to exploit this important information. For language feature extraction, a major problem comes from the long-tail distribution of words in the expressions. Since more than half of the words appear less than 20 times in the public datasets, deep models such as LSTM tend to fail to learn accurate representations for these words. To solve this problem, we propose a word2vec based word mapping method that maps these low frequency words to high frequency words with similar meaning. Experiments show that the proposed method outperforms existing state-of-the-art methods on three referring expression comprehension datasets.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3343031.3351063 | Proceedings of the 27th ACM International Conference on Multimedia |
Keywords | Field | DocType |
referring expression comprehension, semantic visual relationship recognition, word mapping, word2vec | Computer science,Referring expression,Natural language processing,Artificial intelligence,Multimedia,Comprehension | Conference |
ISBN | Citations | PageRank |
978-1-4503-6889-6 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Zhang | 1 | 939 | 103.66 |
| Weiming Li | 2 | 9 | 1.86 |
| Wanli Ouyang | 3 | 2371 | 105.17 |
| Qiang Wang | 4 | 16 | 4.39 |
| Woo-Shik Kim | 5 | 1 | 0.69 |
| Sunghoon Hong | 6 | 1 | 2.04 |