Omkar2118/datasets huggingface dataset:
IndirectRequests
Full name: IndirectRequests

IndirectRequests is an LLM-generated dataset of user utterances in a task-oriented dialogue setting where the user does not directly specify their preferred slot value.

IndirectRequests was generated by crowdsourcing human labels over a dataset generated using a combination of GPT-3.5 (turbo) and GPT-4. Each utterance is labelled along two dimensions:

- **World Understanding** (the degree of world understanding it takes to understand the utterance)
- **Unambiguity** (whether or not the generated utterance unambiguously entails a single target slot value among a set of candidate possible values).
KITTI Odometry Benchmark

The odometry benchmark consists of 22 stereo sequences, saved in loss less png format: We provide 11 sequences (00-10) with ground truth trajectories for training and 11 sequences (11-21) without ground truth for evaluation. For this benchmark you may provide results using monocular or stereo visual odometry, laser-based SLAM or algorithms that combine visual and LIDAR information. The only restriction we impose is that your method is fully automatic (e.g., no manual loop-closure tagging is allowed) and that the same parameter set is used for all sequences. A development kit provides details about the data format. 
More details are available at: https://www.cvlibs.net/datasets/kitti/eval_odometry.php.
ROxford

```misc
@inproceedings{RITAC18,
 author = {Radenovi\'{c}, F. and Iscen, A. and Tolias, G. and Avrithis, Y. and Chum, O.},
 title = {Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking},
 booktitle = {CVPR},
 year = {2018}
}
```

Introducing paper: Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HARPER
Full name: Exploring 3D Human Pose Estimation and Forecasting from the Robot’s Perspective: The HARPER Dataset

We introduce HARPER, a novel dataset for 3D body pose estimation and forecast in dyadic interactions between users and \spot, the quadruped robot manufactured by Boston Dynamics. The key-novelty is the focus on the robot's perspective, i.e., on the data captured by the robot's sensors. These make 3D body pose analysis challenging because being close to the ground captures humans only partially. The scenario underlying HARPER includes 15 actions, of which 10 involve physical contact between the robot and users. The Corpus contains not only the recordings of the built-in stereo cameras of Spot, but also those of a 6-camera OptiTrack system (all recordings are synchronized). This leads to ground-truth skeletal representations with a precision lower than a millimeter. In addition, the Corpus includes reproducible benchmarks on 3D Human Pose Estimation, Human Pose Forecasting, and Collision Prediction, all based on publicly available baseline approaches. This enables future HARPER users to rigorously compare their results with those we provide in this work.

Source: [Download dataset] (https://github.com/intelligolabs/HARPER)

Introducing paper: Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset