Web-Based Semantic Fragment Discovery for On-Line Lingual-Visual Similarity
Authors: Xiaoshuai Sun, Jiewei Cao, Chao Li, Lei Zhu, Heng Tao Shen
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on semantic fragment quality assessment, sentence-based image retrieval, automatic multimedia insertion and ordering demonstrated the effectiveness of the proposed framework. |
| Researcher Affiliation | Academia | 1The University of Queensland, Brisbane 4067, Australia. 2Harbin Institute of Tehcnology, Heilongjiang 150001, China. 3University of Electronic Science and Technology of China, Chengdu 611731, China. |
| Pseudocode | No | The paper describes the methods and formulas (e.g., Q(g) and f̂g pdf(f)) but does not present them in a formally structured pseudocode or algorithm block. |
| Open Source Code | Yes | We make code, datasets and annotations publicly available on our project page. |
| Open Datasets | Yes | We release two new datasets as extensions of Flickr30K (Young et al. 2014) to enable research and comparisons on Web-based unsupervised sentence understanding tasks. Flickr30K-Phrase This dataset consists of 3.2 million images with 32,486 weak phrase labels. Flickr30K-Quality We sample 20K images with 1K phrase labels from Flickr30K-Phrase... |
| Dataset Splits | No | We adopt the 1K test images in Flickr30K for quantitative evaluation. |
| Hardware Specification | No | The paper describes the use of VGG-16 and Mat Conv Net Toolkit but does not provide any specific details about the hardware (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | Practically, we adopt Mat Conv Net Toolkit (Vedaldi and Lenc 2015) with pre-trained model of VGG-16 (Simonyan and Zisserman 2014) for image feature extraction. |
| Experiment Setup | Yes | We set the size of each fragment N = 20, and fix the self-similarity threshold to 0.2 in all the tests. |