uld preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.
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For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).
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If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8.
Experiments compute resources
Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Answer: [Yes]
Justification: In Appendix Section C.
Guidelines:
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The answer NA means that the paper does not include experiments.
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The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
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The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
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The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn’t make it into the paper).
9.
Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines?
Answer: [Yes]
Justification: Yes, the research conducted in this paper fully conforms to the NeurIPS Code of Ethics.
Guidelines:
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The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
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If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.
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The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).
10.
Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [N/A]
Justification: Our method primarily influences the evaluation of large video language models and may encourage the development of stronger video understanding capabilities. It does not have any direct societal impact, nor does it pose any foreseeable negative consequences.
Guidelines:
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The answer NA means that there is no societal impact of the work performed.
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If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
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Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
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The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
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The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
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If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated re