The VideoMem or Video Memorability Database is a collection of sound-less video excerpts and their corresponding ground-truth memorability files. The memorability scores are computed based on the measurement of short-term and long-term memory performances when recognizing small video excerpts a few minutes after viewing them for the short-term case, and 24 to 72 hours later, for the long-term case. It is accompanied with video features extracted from the video excerpts.

It is intended to be used for understanding the memorability of videos and for assessing the quality of methods for predicting the memorability of multimedia content. A detailed description of the benchmark can be found on our Data Description page. The license conditions are mentioned on the Download page.

All documents and papers that report on research that uses the VideoMem Dataset must acknowledge the use of the dataset by including an appropriate citation to the following:


R. Cohendet, C.-H. Demarty, N. Q. Duong and M. Engilberge. VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability. ICCV 2019.


Title = {VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability},

Author = {Romain Cohendet and Claire-H\'{e}l\`{e}ne Demarty and Ngoc Q.K. Duong and Martin Engilberge},

Booktitle = {Proceedings of the International Conference on Computer Vision, Seoul},


R. Cohendet, C.-H. Demarty, N. Q. Duong and M. Engilberge. VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability. 2018. Arxiv:1812.01973.


It would be highly appreciated if this use was shared with InterDigital, at this address: 

, with “VideoMem” in the subject of your email.

The delivered package contains the dataset used in [1] and in the 

MediaEval 2018 Predicting Media Memorability Task

 [2]. It was intended for the tasks of video memorability understanding and prediction. It is composed of:

10,000 short soundless video excerpts split into:

8,000 excerpts that correspond to the development set used for the 

2,000 excerpts that correspond to the test set used for the MediaEval 2018 Predicting Media Memorability Task;

The corresponding ground truth for the 8,000 video excerpts of the development set;

Extracted video features on the 10,000 video excerpts.

You are informed that the original video excerpts remain the ownership of their legitimate owners and that no license is granted on these excerpts. They are provided and may be used exclusively under the article L.122-5 3° a) of the French Code of intellectual property or, where applicable, under the “fair use” doctrine or its equivalent.


Videos were extracted from raw footage used by professionals when creating content. They are varied and contain different scenes types (e.g., animal, food and beverages, nature, people, transportation, etc.). Videos are released in .webm format, with a bitrate of 3,000 kbps. (This format was also used during the annotation process.) They are provided as individual files, named: videoNb.webm

Each video comes with its original title that can be found in file dev-set_video-captions.txt in the package. These titles can often be seen as a list of tags (textual metadata).

The corresponding ground truth for the development set can be found in file ground-truth_dev-set.csv.

It contains one line per video, which consists of:

the number of annotations that was used to calculate the short-term memorability score;

the number of annotations that was used to calculate the long-term memorability score.

A set of pre-extracted visual features is also provided.

Precomputed features are organized in different folders, one per feature. Most of the time the chosen file format is plain text. We specify the internal feature format for each feature file below.

For the following two features, one will find one file per video.

outputs: the final classification layer of the C3D model

feature: a single list of numbers on one line (dimension = 101)

outputs: the histogram of motion patterns for each video

feature: a single list of pairs of numbers with the format: bin: number (dimension = 6075) on one line

The following features were extracted on three key-frames (first (0), one-third (56) and two-thirds (112)) on each video. So there are three files for each video, with names videoNb-0.txt, videoNb-56.txt, videoNb-112.txt.

outputs: histograms of oriented gradients. Gradients are calculated on 32x32 windows on a greyscale image.

feature: a single list of numbers on one line (dimension = depends on the image size)

outputs: Local Binary Patterns. These features represent some local texture information. LBP values are calculated for patches of 8x15 pixels.

outputs: output of the fc7layer of the InceptionV3 deep network.

feature: a single list of pairs of numbers with format imagenet_class:activation (max dimension = 1,000)

 [8] (An efficient alternative to SIFT or SURF)

outputs: Oriented FAST and Rotated BRIEF. ORB is basically a fusion of FAST keypoint detector and BRIEF descriptor with many modifications to enhance the performance.

file format: pickle (extension of file ".p").

feature: a list of keypoints and descriptors (see 

 for some more details). Keypoints and their descriptors were stored using function pickle_keypoints available 

outputs: classic color histogram (three channels)

feature: 3 lists (Red, Green, Blue in that order) of 255 pairs with format bin: number, e.g., 254:1008. One list per line.

 [9]. Those features were extracted for one frame every 10 frames of one single video and then aggregated through the computation of their mean and median values.

outputs: a collection of features used in the prediction of visual aesthetics, composed of color, texture, and object-based descriptors, aggregated at video level by median and mean methods.

feature: a single list of comma-separated numbers on one line (dimension = 109)

We would like to thank Ricardo Manhaes Savii (Federal University of São Paulo) for extracting most of the features. His code to extract the features is available 

. We also want to thank Mihai Gabriel Constantin (

[1] Cohendet, R., Demarty, C.-H., Duong N. Q. and Engilberge, M.. VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability. 2018. Arxiv:1812.01973.

[2] Cohendet, R., Demarty, C.-H., Duong N. Q, Sjöberg, M., Ionescu, B., Do, T.-T. MediaEval 2018: Predicting Media Memorability. Proceedings of the MediaEval Workshop, Sophia Antipolis, France, 2018.

[3] Tran, D., Bourdev, L., Fergus, R., Torresani, L. and Paluri, M. (2015). 

Learning Spatiotemporal Features with 3D Convolutional Networks

. In Computer Vision (ICCV), 2015 IEEE International Conference on (pp. 4489-4497).

[4] Almeida, J., Leite, N. J., and Torres, R. D. S. (2011). 

Comparison of video sequences with histograms of motion patterns

. In Image Processing (ICIP), 2011 18th IEEE International Conference on (pp. 3673-3676).

Histograms of oriented gradients for human detection

. In Computer Vision and Pattern Recognition, 2005 (CVPR). In IEEE Computer Society Conference on (pp. 886-893).

Texture unit, texture spectrum, and texture analysis

. In IEEE transactions on Geoscience and Remote Sensing, 28(4), (pp. 509-512).

[7] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). 

Rethinking the inception architecture for computer vision

. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2818-2826).

[8] Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011). 

ORB: An efficient alternative to SIFT or SURF

. In Computer Vision (ICCV), 2011 IEEE international conference on (pp. 2564-2571).

[9] Haas, A.F., Guibert, M., Foerschner, A., Co, T., Calhoun, S., George, E., Hatay, M., Dinsdale, E., Sandin, S.A., Smith, J.E., Vermeij, M.J.A., Felts, B., Dustan, P., Salamon, P., Rohwer, F. (2015) 

Can we measure beauty? Computational evaluation of coral reef aesthetics

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To download the VideoMem dataset please send an email to 

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” means the database which consists of:

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The corresponding ground truth for 8,000 out of the 10,000 excerpts (i.e., for each excerpt, a short-term memorability score, a long term-memorability score and the number of annotations in both cases);

Video features that were extracted from the video excerpts.

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The dataset was provided by InterDigital and is described in the following publication:


Cohendet, R., Demarty, C.-H., Duong N. Q. and Engilberge, M.. VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability. ICCV 2019.


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Video Memorability Dataset

Video Memorability Dataset

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