├── stats.png
├── examples.png
├── combined_lengths.png
├── minerva_examples.png
├── .gitignore
├── CONTRIBUTING.md
├── README.md
└── LICENSE
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # Distribution / packaging
7 | .Python
8 | build/
9 | develop-eggs/
10 | dist/
11 | downloads/
12 | eggs/
13 | .eggs/
14 | lib/
15 | lib64/
16 | parts/
17 | sdist/
18 | var/
19 | wheels/
20 | share/python-wheels/
21 | *.egg-info/
22 | .installed.cfg
23 | *.egg
24 | MANIFEST
25 |
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/CONTRIBUTING.md:
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1 | # How to Contribute
2 |
3 | ## Contributor License Agreement
4 |
5 | Contributions to this project must be accompanied by a Contributor License
6 | Agreement. You (or your employer) retain the copyright to your contribution,
7 | this simply gives us permission to use and redistribute your contributions as
8 | part of the project. Head over to to see
9 | your current agreements on file or to sign a new one.
10 |
11 | You generally only need to submit a CLA once, so if you've already submitted one
12 | (even if it was for a different project), you probably don't need to do it
13 | again.
14 |
15 | ## Code reviews
16 |
17 | All submissions, including submissions by project members, require review. We
18 | use GitHub pull requests for this purpose. Consult
19 | [GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
20 | information on using pull requests.
21 |
22 | ## Community Guidelines
23 |
24 | This project follows [Google's Open Source Community
25 | Guidelines](https://opensource.google/conduct/).
26 |
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/README.md:
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1 | # Neptune Dataset Collection
2 |
3 | ## Tl;dr
4 | This page covers the Neptune Dataset Collection which is a set of video QA
5 | datasets.
6 | This collection currently includes the original [Neptune](#neptune) dataset and
7 | the [MINERVA](#minerva) dataset.
8 |
9 | ## MINERVA
10 | MINERVA consists of ~1.5K
11 | challenging question-answer-decoy (QAD) sets for variable length videos. For
12 | each question, we provide 5 answer choices, as well as detailed,
13 | manually-annotated reasoning traces. Every question in MINERVA requires complex
14 | reasoning using two or more skills
15 | (for example numerical reasoning, temporal reasoning, spatial navigation).
16 | Videos also span multiple domains (short films, sports, instructional videos
17 | etc), with various video lengths (from 2 minutes to over 1.5 hours). The
18 | hand-crafted, detailed reasoning trace accompanying each question outlines
19 | the steps that are required to come to the correct answer.
20 | These traces include timestamps where necessary to refer to relevant sections of
21 | the video, and also describes key actions, objects, as well as outlines logical
22 | reasoning steps. More details are provided in our
23 | [arXiv](https://arxiv.org/abs/2505.00681) paper.
24 |
25 |
26 |
27 |
28 |
29 | Examples from MINERVA. MINERVA consists of challenging
30 | question-answer-decoy sets for videos. The answer to each question is
31 | also accompanied by a detailed reasoning trace, which outlines the steps
32 | required to come to the answer. Reasoning traces are detailed,
33 | including timestamps (highlighted in green) and key actions
34 | (highlighted in pink).
35 |
36 |
37 |
38 |
39 |
40 |
41 |
42 |
43 |
44 | MINERVA covers a variety of video lengths. Reasoning traces are long and detailed.
45 |
46 |
47 |
48 |
49 | ### Downloading the Data
50 | We provide a json file that contains the YouTube IDs and annotations.
51 |
52 | The json file contains the following fields:
53 |
54 | - key: Unique identifier for each question
55 | - video_id: YouTube URL
56 | - question: Free-form question
57 | - answer: Free-form answer
58 | - answer_choice_{i}: Decoys for MCQ evaluation, i in range(0,4)
59 | - answer_id: ID of the correct answer in the decoys
60 | - reasoning: Detailed reasoning trace
61 | - question type: A comma-separated list of multiple skills needed to answer the
62 | question
63 | - split: Coarse video domain
64 | - category: Fine-grained video domain
65 |
66 | [MINERVA json](https://storage.mtls.cloud.google.com/neptunedata/minerva.json)
67 |
68 | ### Citing this work
69 |
70 | ```latex
71 | @article{minerva25,
72 | title={MINERVA: Evaluating Complex Video Reasoning},
73 | author={Nagrani, Arsha and Menon, Sachit and Iscen, Ahmet and Buch, Shyamal and Mehran, Ramin and Jha, Nilpa and Hauth, Anja and Zhu, Yukun and Vondrick, Carl and Sirotenko, Mikhail and Schmid, Cordelia and Weyand, Tobias},
74 | journal={arXiv preprint arXiv:2505.00681},
75 | year={2025}
76 | }
77 | ```
78 |
79 | ## Neptune
80 | Neptune is a dataset consisting of
81 | challenging question-answer-decoy (QAD) sets
82 | for variable length videos (up to 15 minutes). The goal of this dataset is to
83 | test video-language models for a broad range of long video reasoning abilities,
84 | which are provided as "question type" labels for each question, for example
85 | "video summarization", "temporal ordering", "state changes" and "creator intent"
86 | amongst others. More details are provided in our
87 | [arXiv](https://www.arxiv.org/abs/2412.09582) paper.
88 |
89 |
90 |
91 |
92 |
93 | Neptune consists of challenging question-answer-decoy sets for videos
94 | to assess a number of long video reasoning abilities.
95 |
96 |
97 |
98 |
99 | Neptune allows for two modes of evaluation: multiple-choice and
100 | open-ended question answering. For the latter, we provide our own open-ended
101 | metric based on Gemma, called Gemma Equivalence Metric (GEM).
102 |
103 | Neptune was created using a semi-automatic pipeline, which involves careful
104 | prompting of large LLMs and VLMs, including Gemini. See more details provided
105 | in the [paper](https://www.arxiv.org/abs/2412.09582).
106 |
107 | Neptune has more than 3,200 questions for over 2,400 videos.
108 |
109 |
110 |
111 |
112 |
113 | Greater than 12% of the videos are longer than 5 minutes and over 25%
114 | are longer than 3 minutes. Neptune covers a number of question types
115 | and video domains.
116 |
117 |
118 |
119 |
120 | ### Downloading the Data
121 |
122 | We provide links to json files that contain the YouTube IDs and annotations for
123 | each split below.
124 | Please see the paper for details regarding each split.
125 |
126 | The json files contains the following fields:
127 |
128 | - key: Unique identifier for each question
129 | - video_id: YouTube URL
130 | - question: Free-form question
131 | - answer: Free-form answer
132 | - answer_choice_{i}: Decoys for MCQ evaluation, i in range(0,4)
133 | - answer_id: ID of the correct answer in the decoys
134 | - question type: Question type
135 |
136 | [Neptune-Full](https://storage.mtls.cloud.google.com/neptunedata/neptune_full.json)
137 |
138 | [Neptune-MMH](https://storage.mtls.cloud.google.com/neptunedata/neptune_mmh.json)
139 |
140 | [Neptune-MMA](https://storage.mtls.cloud.google.com/neptunedata/neptune_mma.json)
141 |
142 | ### Evaluation and Metrics
143 |
144 | Multiple choice evaluation involves selecting the answer from 5 options
145 | (including 4 decoys) and using accuracy as the metric.
146 |
147 | For open-ended evaluation, we create a new language model based metric, called
148 | the Gemma Equivalence Metric (GEM). We do this by fine tuning a Gemma
149 | checkpoint on the
150 | [BEM answer equivalence dataset](https://github.com/google-research-datasets/answer-equivalence-dataset)
151 | and prompt it to determine if a produced answer is equivalent to the ground
152 | truth.
153 |
154 | ### Citing this work
155 |
156 | ```latex
157 | @article{neptune24,
158 | title={Neptune: The Long Orbit to Benchmarking Long Video Understanding},
159 | author={Nagrani, Arsha and Zhang, Mingda and Mehran, Ramin and Hornung, Rachel and Gundavarapu, Nitesh Bharadwaj and Jha, Nilpa and Myers, Austin and Zhou, Xingyi and Gong, Boqing and Schmid, Cordelia and Sirotenko, Mikhail and Zhu, Yukun and Weyand, Tobias},
160 | journal={arXiv preprint arXiv:2412.09582},
161 | year={2024},
162 | }
163 | ```
164 |
165 | ## License and disclaimer
166 | Copyright 2024 DeepMind Technologies Limited
167 |
168 | All software is licensed under the Apache License, Version 2.0 (Apache 2.0);
169 | you may not use this file except in compliance with the Apache 2.0 license.
170 | You may obtain a copy of the Apache 2.0 license at:
171 | https://www.apache.org/licenses/LICENSE-2.0
172 |
173 | All other materials are licensed under the Creative Commons Attribution 4.0
174 | International License (CC-BY). You may obtain a copy of the CC-BY license at:
175 | https://creativecommons.org/licenses/by/4.0/legalcode
176 |
177 | Unless required by applicable law or agreed to in writing, all software and
178 | materials distributed here under the Apache 2.0 or CC-BY licenses are
179 | distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
180 | either express or implied. See the licenses for the specific language governing
181 | permissions and limitations under those licenses.
182 |
183 | This is not an official Google product.
184 |
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