cff-version: 1.2.0
message: "If you use this dataset, please cite as below."
title: "NExT-CF: A Counterfactual Entity-Removal Benchmark for Video Question Answering"
authors:
  - name: "Anonymous Authors (submission under review)"
year: 2026
version: "1.0.0"
license: "CC-BY-NC-SA-4.0"
type: dataset
abstract: >
  NExT-CF is a benchmark of 570 counterfactually edited videos derived from NExT-QA,
  used to probe whether Video Question Answering models rely on visual evidence
  or language priors. Each video has a target entity removed via SAM3 segmentation
  and DiffuEraser diffusion inpainting, with the corresponding QA pairs carried
  over from NExT-QA.
keywords:
  - video question answering
  - counterfactual
  - benchmark
  - diagnostic benchmark
  - visual grounding
  - evidence reliance
  - diffusion inpainting
references:
  - type: conference-paper
    authors:
      - family-names: Xiao
        given-names: Junbin
      - family-names: Shang
        given-names: Xindi
      - family-names: Yao
        given-names: Angela
      - family-names: Chua
        given-names: Tat-Seng
    title: "NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions"
    conference:
      name: "IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"
    year: 2021
    start: 9777
    end: 9786
    notes: "arXiv:2105.08276; project repo: https://github.com/doc-doc/NExT-QA"
notes: >
  NeurIPS 2026 Evaluations & Datasets track submission (under review).
  Author identities will be added upon acceptance.