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CoMplex video Object SEgmentation (MOSE) is a dataset to study the tracking and segmenting objects in complex environments. MOSE contains 2,149 video clips and 5,200 objects from 36 categories, with 431,725 high-quality object segmentation masks. The most notable feature of MOSE dataset is complex scenes with crowded and occluded objects. Source: MOSE: A New Dataset for Video Object Segmentation in Complex Scenes

CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) is the largest dataset of sentence-level sentiment analysis and emotion recognition in online videos. CMU-MOSEI contains over 12 hours of annotated video from over 1000 speakers and 250 topics.

The set is based on the ZINC Clean Leads collection. It contains 4,591,276 molecules in total, filtered by molecular weight in the range from 250 to 350 Daltons, a number of rotatable bonds not greater than 7, and XlogP less than or equal to 3.5. We removed molecules containing charged atoms or atoms besides C, N, S, O, F, Cl, Br, H or cycles longer than 8 atoms. The molecules were filtered via medicinal chemistry filters (MCFs) and PAINS filters. The dataset contains 1,936,962 molecular structures. For experiments, we split the dataset into a training, test and scaffold test sets containing around 1.6M, 176k, and 176k molecules respectively. The scaffold test set contains unique Bemis-Murcko scaffolds that were not present in the training and test sets. We use this set to assess how well the model can generate previously unobserved scaffolds.

The MISAW data set is composed of 27 sequences of micro-surgical anastomosis on artificial blood vessels performed by 3 surgeons and 3 engineering students. The dataset contained video, kinematic, and procedural descriptions synchronized at 30Hz. The procedural descriptions contained phases, steps, and activities performed by the participants. Source: MIcro-Surgical Anastomose Workflow recognition challenge report

InfantMarmosetsVox is a dataset for multi-class call-type and caller identification. It contains audio recordings of different individual marmosets and their call-types. The dataset contains a total of 350 files of precisely labelled 10-minute audio recordings across all caller classes. The audio was recorded from five pairs of infant marmoset twins, each recorded individually in two separate sound-proofed recording rooms at a sampling rate of 44.1 kHz. The start and end time, call-type, and marmoset identity of each vocalization are provided, labeled by an experienced researcher. A PyTorch Dataloader is included in this dataset.

All animal procedures are overseen by veterinary staff of the MIT and Broad Institute Department of Comparative Medicine, in compliance with the NIH guide for the care and use of laboratory animals and approved by the MIT and Broad Institute animal care and use committees. Video of common marmosets (Callithrix jacchus) was collected in the laboratory of Guoping Feng at MIT. Marmosets were recorded using Kinect V2 cameras (Microsoft) with a resolution of 1080p and frame rate of 30 Hz. After acquisition, images to be used for training the network were manually cropped to 1000 x 1000 pixels or smaller. The dataset is 7,600 labeled frames from 40 different marmosets collected from 3 different colonies (in different facilities). Each cage contains a pair of marmosets, where one marmoset had light blue dye applied to its tufts. One human annotator labeled the 15 marker points on each animal present in the frame (frames contained either 1 or 2 animals). https://benchmark.deeplabcut.org/datasets.html

Please find more details of this dataset at https://alex-xun-xu.github.io/ProjectPage/CVPR_18/index.html 3D motion segmentation has been the key problem in computer vision research due to the application in structure from motion and robotics. Traditional motion segmentation approaches are often evaluated on artificial dataset like Hopkins 155 [1] and its variants. Because the vanishing camera translation effect is often overlooked, these approaches would fail in real world scenes where camera is carrying out significant translation and scene has complex structure. We proposed the KT3DMoSeg to address the 3D motion segmentation problem in real world scenes. The KT3DMoSeg dataset was created upon the KITTI benchmark [2] by manually selecting 22 sequences and labelling each individual foreground object. We select sequence with more significant camera translation so camera mounted on moving cars are preferred. We are interested in the interplay of multiple motions, so clips with more than 3 motions are also chosen, as long as these moving objects contain enough features for forming motion hypotheses. 22 short clips, each with 10-20 frames, are chosen for evaluation. We extract dense trajectories from each sequence using [3] and prune out trajectories shorter than 5 frames. Reference [1] R. Tron and R. Vidal. A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms. CVPR, 2007. [2] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The kitti dataset. International Journal of Robotics Research, 2013. [3] N. Sundaram, T. Brox, and K. Keutzer. Dense point trajectories by GPU-accelerated large displacement optical flow. In ECCV, 2010.