Wen Hua Lin, Kuan-Ting Chen, HungYueh Chiang, Winston H. Hsu. Netizen-Style Commenting on Fashion Photos – Dataset and Diversity Measures, WWW 2018 [arXiv pdf]
To the best of our knowledge, this is the first and the largest netizen-style commenting dataset. It contains 355,205 images from 11,034 users and 5 million associated comments collected from Lookbook. As the examples shown in Figure 1, most of the images are fashion photos in various angles of views, distinct filters and different styles of collage. As Figure 2 (b) shows, each image is paired with (diverse) user comments, and the average number of comments is 14 per image in our dataset. Besides, each post has a title named by an author, a publishing date and the number of hearts given by other users. Moreover, some users add names, brands, pantone of the clothes, and stores where they bought the clothes. Furthermore, we collect the authors’ public information. Some of them contain age, gender, country and the number of fans (cf., Figure 3). In this paper, we only use the comments and the photos from our dataset. Other attributes can be used to refine the system in future work. For comparing the results on Flickr30k, we also sampled 28,000 for training, 1,000 for validation and 1,000 for testing. Besides, we also sampled five comments for each image.
Compared to general image captioning datasets such as Flickr30k (Rashtchian et al. 2010), the data from social media are quite noising, full of emojis, emoticons, slang and much shorter (cf., Figure 2 (b) and Table 1), which makes generating a vivid netizen style comment much more challenging. Moreover, plenty of photos are in different styles of collage (cf., photos in Figure 1). Therefore, it makes the image features much more noising than single view photos.
|Dataset||Images||Sentences||Average Length||Unique words|
To demonstrate the effect of diverse comments, we conducted a user study from 23 users. The users are about 25 year-old and familiar with netizen style community and social media. The sex ratio in our user study is 2.83 males/female. They are asked to rank comments for 35 fashion photos. Each photo has 4 comments — from one randomly picked human comments, NC, Attention and our NSCNC. Therefore, each of the users has to appraise 140 comments generated from different methods. Furthermore, we collect user feedback to understand user judgements on comments generated by different methods. According to the result, our style-weight makes captioning model mimic human style and generates human-like comments which most people agree with in our user study.
This work was supported in part by Microsoft Research Asia and the Ministry of Science and Technology, Taiwan, under Grant MOST 105-2218-E-002-032. We also benefit from the grants from NVIDIA and the NVIDIA DGX-1 AI Supercomputer and the discussions with Dr. Ruihua Song, Microsoft Research Asia.