# Topic-Oriented Text Features Can Match Visual Deep Models of Video Memorability
## 3 Datasets
### VideoMem  
Introduced in 2018 by [PERSON] et al., VideoMem is composed of 10,000 videos, with memorability scores attached as ground-truth labels. It represents the first attempt to build a large-scale corpus regarding video visual memory, which were typically limited to a few hundred of samples in previous works. Added to this, videos were not collected from TRECVID or Hollywood-like films [21; 22], but extracted from raw footage originally designed to serve as generic material for professional editing of films or advertisements. Clips are of high quality (HD) and are provided as short soundless videos of 7 s in _.webm_ format, with a bitrate of 3000 kps for 24 fps. The rationale behind this is to have a diverse dataset in terms of topics while minimizing side-effects derived from having more than one semantic unit within the same video. All of them are briefly described by a caption that sums up the visual semantics of the scene.  
Its authors also adapted an annotation protocol previously developed to image memorability to the task of video memorability [1; 4]. They gathered human participants in sessions during which they were presented a series of videos, and researchers asked them to hit a button every time they remembered having watched any video previously projected within the same session. Participants carried out this procedure twice. Whereas in the first one they had to respond to videos within the same session, therefore providing a label for the short-term memory (understood as memorability after a few minutes), in the second session the task referred to the videos projected during the first session. This second phase was carried out between 24 and 72 h after the first one, and it can be thought of as a long-term memorability score. Not all participants went through this second phase. This caused an inconsistency in the number of annotations per video between the short-term and the long-term cases. On average, every video was seen as a repeated target by 38 people to estimate the short-term labels whereas only 13 did this for the long-term ones. In both cases the final memorability score of a video is computed as the average percentage of people that successfully recognised having seen a video. Videos are never presented in the same fixed order, but randomly. Because it has been observed that video memorability depends in a linear way on the number of videos between two occurrences [19; 20], in order to homogenize memorability labels among videos a linear correction (firstly introduced in [16]) is applied to the raw hit scores, thus obtaining the final set of labels.  
However, as can be noticed from Figure 1, there is not a clear relationship between short and long term memorability scores. A video that is on average well remembered in the short-run does not necessarily remain in memory a couple of days after with the same intensity. Likewise, clips that may go unnoticed in a first stage can instead trigger a quick and clear memory during the long-term session. Because of this apparent lack of correlation between them, in this study we tackle them as two distinct problems.