Omkar2118/datasets huggingface dataset:
PhoCAL

Object pose estimation is crucial for robotic applications and augmented reality. To provide a benchmark with high-quality ground truth annotations to the community, we introduce a multimodal dataset for category-level object pose estimation with photometrically challenging objects termed PhoCaL. PhoCaL comprises 60 high quality 3D models of household objects over 8 categories including highly reflective, transparent and symmetric objects. We developed a novel robot-supported multi-modal (RGB, depth, polarisation) data acquisition and annotation process. It ensures sub-millimeter accuracy of the pose for opaque textured, shiny and transparent objects, no motion blur and perfect camera synchronisation.

Introducing paper: PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects
Test-of-Time
Full name: Test of Time Synthetic Video Dataset

The goal of this dataset is to probe video-language models for understanding of simple temporal relations like "before" and "after". The dataset  is only meant to be an evaluation set and not a training set. 

Contents:
1. The dataset has synthetic videos which consists of a pair of shapes appearing gradually. For example, video for the caption "a red circle appears after a yellow circle" will first show a "yellow circle" appear and then a "red circle" appear. The model has to determine the right caption in comparison with a distractor caption "a yellow circle appears after a red circle". Note that this distractor caption has the same set of words but in a different order, motivated by the Winograd schema.
2. The dataset also has a control set in which videos only have a single event, e.g., "a red circle appears". Note that this is a control task to ensure that these videos are not out-of-distribution for a given video model.
A time-aware model shall perform perfectly well on both sets.  A space-aware model that is not time-aware shall perform poorly on the temporal task while performing perfectly on the control task.

Introducing paper: Test of Time: Instilling Video-Language Models with a Sense of Time
Beijing Traffic

The Beijing Traffic Dataset collects traffic speeds at 5-minute granularity for 3126 roadway segments in Beijing between 2022/05/12 and 2022/07/25.

Introducing paper: MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation