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A set of patterns used in psychophysical research to evaluate the ability of saliency algorithms to find targets distinct from distractors in orientation, color and size. Each image is a 7x7 grid and contains a single target. All images are 1024x1024px and have corresponding ground truth masks for the target and distractors.

xP3 is a multilingual dataset for multitask prompted finetuning. It is a composite of supervised datasets in 46 languages with English and machine-translated prompts. Source: Crosslingual Generalization through Multitask Finetuning Image Source: https://arxiv.org/pdf/2211.01786v1.pdf

P3M-10k contains 10421 high-resolution real-world face-blurred portrait images, along with their manually labeled alpha mattes. The Dataset is aimed to aid research efforts in the area of portrait image matting and related topics.

Starting from the Panoptic Dataset, we use the PanopTOP framework to generate the PanopTOP31K dataset, consisting of 31K images from 23 different subjects recorded from diverse and challenging viewpoints, also including the top-view.

CoP3D is a collection of crowd-sourced videos showing around 4,200 distinct pets. CoP2D is a large-scale datasets for benchmarking non-rigid 3D reconstruction "in the wild".

WiGesture dataset contains data related to gesture recognition and people id identification in a meeting room scenario. The dataset provides synchronised CSI, RSSI, and timestamp for each sample. It can be used for research on WiFi-based human gesture recognition and people id identification.

Python Programming Puzzles (P3) is an open-source dataset where each puzzle is defined by a short Python program , and the goal is to find an input which makes output "True". The puzzles are objective in that each one is specified entirely by the source code of its verifier, so evaluating is all that is needed to test a candidate solution. They do not require an answer key or input/output examples, nor do they depend on natural language understanding. The dataset is comprehensive in that it spans problems of a range of difficulties and domains, ranging from trivial string manipulation problems that are immediately obvious to human programmers (but not necessarily to AI), to classic programming puzzles (e.g., Towers of Hanoi), to interview/competitive-programming problems (e.g., dynamic programming), to longstanding open problems in algorithms and mathematics (e.g., factoring). The objective nature of P3 readily supports self-supervised bootstrapping. Source: Programming Puzzles

WiFall dataset contains data related to fall detection, action recognition and people id identification in a meeting room scenario. The dataset provides synchronised CSI, RSSI, and timestamp for each sample.

This database contains EEG and evoked potential recordings from 20 participants. This allows to assess the signal to noise ratio: - Signal: The P300 power and VEP power can be used to assess the signal power - Noise: The signal power consisting of EMG and baseline EEG during the different tasks allows to determine the noise level

The scene derives from photo-realistic MP3D datasets. Our dataset offers a wide variety of environments especially for Social Navigation tasks, with carefully calibrated human density, incorporating realistic human motions and natural movement patterns. These features ensure balanced interaction dynamics across diverse scenes, facilitating the development of more effective social navigation algorithms.

This datasets consists of challenging reasoning questions in multiple choice format.