Omkar2118/datasets huggingface dataset:
VSTaR-1M

VSTaR-1M is a 1M instruction tuning dataset, created using Video-STaR, with the source datasets: 
* [Kinetics700](https://github.com/cvdfoundation/kinetics-dataset)
* [STAR-benchmark](https://bobbywu.com/STAR/)
* [FineDiving](https://finediving.ivg-research.xyz)

The videos for VSTaR-1M can be found in the links above. 

VSTaR-1M is built off of diverse task with the goal of enhancing video-language alignment in Large Video-Language Models (LVLMs).

* kinetics700_tune_.json - Instruction tuning QA pairs for the Kinetics700 source dataset. Good for increasing diversity and for more fine-grained activity recognition.  
* starb_tune_.json - Instruction tuning QA pairs for the STAR-benchmark source dataset. Good for temporal reasoning.
* finediving_tune_.json - Instruction tuning QA pairs for the FineDiving source dataset. Example of adapting LVLMs for novel tasks (Olympic diving judge).

Introducing paper: Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision
MuseChat Dataset
Full name: MuseChat: A Conversational Music Recommendation System for Videos (CVPR 2024 Highlight Paper)

Music recommendation for videos attracts growing interest in multi-modal research. However, existing systems focus primarily on content compatibility, often ignoring the users’ preferences. Their inability to interact with users for further refinements or to provide explanations leads to a less satisfying experience. We address these issues with MuseChat, a first-of-its-kind dialogue-based recommendation system that personalizes music suggestions for videos. Our system consists of two key functionalities with associated modules: recommendation and reasoning. The recommendation module takes a video along with optional information including previous suggested music and user’s preference as inputs and retrieves an appropriate music matching the context. The reasoning module, equipped with the power of Large Language Model (Vicuna-7B) and extended to multi-modal inputs, is able to provide reasonable explanation for the recommended music. To evaluate the effectiveness of MuseChat, we build a large-scale dataset, conversational music recommendation for videos,
that simulates a two-turn interaction between a user and a recommender based on accurate music track information. Experiment results show that MuseChat achieves significant improvements over existing video-based music retrieval methods as well as offers strong interpretability and interactability. The dataset of this work is available at https://dongzhikang.github.io/musechat.

Introducing paper: MuseChat: A Conversational Music Recommendation System for Videos
Co-RaL-Dataset

This repository includes the experimental dataset acquired to evaluate our radar-leg odometry algorithm, Co-RaL, accepted by IEEE IROS 2024. Our dataset includes sensor data of chip radar, imu, velodyne, and kinematic data from Boston Dynamics SPOT (Joint encoders and contact sensors). Each sequence is acquired with different environments to evaluate the algorithm performance generally. The dataset is provided with ROS Bag file format.

Introducing paper: Co-RaL: Complementary Radar-Leg Odometry with 4-DoF Optimization and Rolling Contact