데이터셋 탐색

OVIS is a new large scale benchmark dataset for video instance segmentation task. It is designed with the philosophy of perceiving object occlusions in videos, which could reveal the complexity and the diversity of real-world scenes. OVIS consists of: * 296k high-quality instance masks * 25 commonly seen semantic categories * 901 videos with severe object occlusions * 5,223 unique instances If the description or image is from a different paper, please refer to it as follows: Source: http://songbai.site/ovis/

AstroVision is a large-scale dataset comprised of 115,970 densely annotated, real images of 16 different small bodies from both legacy and ongoing deep space missions to facilitate the study of deep learning for autonomous navigation in the vicinity of a small body.

BIWI 3D corpus comprises a total of 1109 sentences uttered by 14 native English speakers (6 males and 8 females). A real time 3D scanner and a professional microphone were used to capture the facial movements and the speech of the speakers. The dense dynamic face scans were acquired at 25 frames per second and the RMS error in the 3D reconstruction is about 0.5 mm. In order to ease automatic speech segmentation, we carried out the recordings in a anechoic room, with walls covered by sound wave-absorbing materials. Each sentence was recorded twice: - First, the speaker read the sentence from text, with a neutral expression. - Then, the speaker watched a clip extracted from a feature film where the sentence is acted by professional actors and the context is highly emotional. After rating the emotions induced by the video, the speaker repeated the sentence.

PACS (Physical Audiovisual CommonSense) is the first audiovisual benchmark annotated for physical commonsense attributes. PACS contains a total of 13,400 question-answer pairs, involving 1,377 unique physical commonsense questions and 1,526 videos. The dataset provides new opportunities to advance the research field of physical reasoning by bringing audio as a core component of this multimodal problem.

Audiovisual Moments in Time (AVMIT) is a large-scale dataset of audiovisual action events. The dataset includes the annotation of 57,177 audiovisual videos from the Moments in Time dataset, each independently evaluated by 3 of 11 trained participants. Each annotation pertains to whether the labelled audiovisual action event is present and whether it is the most prominent feature of the video. The dataset also provides a curated test set of 960 videos across 16 classes, suitable for comparative experiments involving computational models and human participants, specifically when addressing research questions where audiovisual correspondence is of critical importance.

Eurovision 2018 official votes dataset. more details can be found here: https://towardsdatascience.com/social-network-analysis-from-theory-to-applications-with-python-d12e9a34c2c7

Quality of Experience Evaluation of Interactive Virtual Environments with Audiovisual Scenes (QoEVAVE) provides an initial audiovisual database consiting of 12 sequences capturing real-life nature and urban scenes. The maximum video resolution is 7680x3840 (8k) at 60 frames-per-second, with 4th-order Ambisonics spatial audio (4OA). All video sequences are recorded with a minumum target duration of 60 seconds and designed to represent real-life settings for systematically evaluating various dimensions of uni-/multimodal perception, cognition, behavior, and quality of experience (QoE) in a controlled virtual environment. This database serves as a novel high-quality reference material with an equal focus on auditory and visual sensory information within the QoE community. Source: QoEVAVE Database Documentation Image Source: QoEVAVE Database Documentation

SpectroVision is a dataset of 14,400 high resolution texture images and spectral measurements collected from a PR2 mobile manipulator that interacted with 144 household objects from eight material categories. Source: https://github.com/Healthcare-Robotics/spectrovision Image Source: https://github.com/Healthcare-Robotics/spectrovision

This dataset is from Hartmann and Kemmerzell (2010), who, among other things, analyze the causes of the emergence of party ban provisions in sub-Saharan Africa.