Anetqa Eval

anetqa-eval

ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos — Zhou Yu et al. (2023) (arXiv:2305.02519, 2023)

What this evaluates

Evaluates fine-grained compositional reasoning over untrimmed videos by requiring models to interpret spatio-temporal scene graphs and answer complex questions involving attributes, actions, and temporal relationships.

Datasets

• ANetQA — total ?; splits: train (-1), val (-1), test (-1)

Metrics

• accuracy (primary) — range: percent
  • Calculated as the percentage of correctly predicted answers out of the total number of questions. Per-type accuracies are also reported under four taxonomies: question structures, question semantics, reasoning skills, and answer types.

Input / output format

Input: Untrimmed video and a natural language question.

Output: A single predicted answer (binary choice, open-ended text, or one of four choices: [A], [B], both, or none).

Scoring recipe

correct = 0
total = 0
for pred, gold in zip(predictions, gold_answers):
    if pred == gold:
        correct += 1
    total += 1
accuracy = (correct / total) * 100

Common pitfalls

• Models may over-rely on language priors rather than visual reasoning, though balancing strategies are used to mitigate this.
• Frame sampling strategy heavily impacts performance; insufficient or non-uniform sampling often misses critical temporal cues needed for fine-grained reasoning.
• Open-ended and attribute-oriented questions are significantly harder than binary/choice questions, leading to sharp accuracy drops.

Evidence (verbatim from paper)

Besides the overall accuracy, we follow [[9]] to report the per-type accuracies under different taxonomies, i.e., question structures, question semantics, reasoning skills, and answer types.

Citation

@misc{yu2023anetqa,
  title={ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos},
  author={Zhou Yu et al. (2023)},
  year={2023},
  note={arXiv:2305.02519}
}

• arXiv: 2305.02519