Omkar2118/datasets huggingface dataset:
OPD Dataset
Full name: OPD Articulated Object Dataset

The link includes both our OPDSynth and OPDReal dataset. For OPDSynth, we select objects with openable parts from an existing dataset of articulated 3D models PartNet-Mobility. For OPDReal, we reconstruct 3D polygonal meshes for articulated objects in real indoor environments and annotate their parts and articulation information.

Introducing paper: OPD: Single-view 3D Openable Part Detection
MedQA

Multiple choice question answering based on the United States Medical License Exams (USMLE). The dataset is collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively.

Introducing paper: What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
CurveLanes

CurveLanes is a new benchmark lane detection dataset with 150K lanes images for difficult scenarios such as curves and multi-lanes in traffic lane detection. It is collected in real urban and highway scenarios in multiple cities in China. It is the largest lane detection dataset so far and establishes a more challenging benchmark for the community.

We separate the whole dataset 150K into three parts: train:100K, val: 20K and testing: 30K. The resolution of most images in this dataset is 2650×1440.

For each image, we manually annotate all lanes in image with natural cubic splines. All images are carefully selected so that most of them image contains at least one curve lane. More difficult scenarios such as S-curves, Y-lanes, night and multi-lanes (the number of lane lines is more than 4) can be found in this dataset.

Introducing paper: CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
HC-STVG1
Full name: Human-centric Spatio-Temporal Video Grounding

The newly proposed HC-STVG task aims to localize the target person spatio-temporally in an untrimmed video. For this task, we collect a new benchmark dataset, which has spatio temporal annotations related to the target persons in complex multi-person scenes, together with full interaction and rich action information.

Introducing paper: Human-centric Spatio-Temporal Video Grounding With Visual Transformers
HC-STVG2

We have added data and cleaned the labels in HC-STVG to build the HC-STVG2.0. While the original database contained 5660 videos, the new database has been re-annotated and modified and now contains 16,000 + videos for this challenge.

Introducing paper: Human-centric Spatio-Temporal Video Grounding With Visual Transformers