Few Shot Action Recognition Eval

few-shot-action-recognition-eval

Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition — Pulkit Kumar et al. (2025) (arXiv:2508.03695, 2025)

What this evaluates

Evaluates a model's ability to recognize human actions in video clips using only a few labeled examples per class. It probes the model's capacity to leverage motion dynamics and semantic cues for robust classification under data-scarce conditions.

Datasets

• Something-Something — total ?; splits: Small (10000), Full (-1)
• Kinetics — total ?; splits: few-shot (-1)
• UCF101 — total ?; splits: few-shot (-1)
• HMDB51 — total ?; splits: few-shot (-1)
• FineGym — total ?; splits: few-shot (-1)

Metrics

• average few-shot accuracy (primary) — range: percent
  • Accuracy averaged over 10,000 evaluation episodes. Each episode follows a standard N-way K-shot protocol where the model classifies query videos into one of N classes using K support examples per class.

Input / output format

Input: Video clips representing human actions, processed through a DINOv2 feature extractor and CoTracker to generate semantic-aware trajectory points.

Output: Predicted class label for each query video clip.

Scoring recipe

accuracy = 0
for episode in 10000_episodes:
  support_set = sample(K examples per class from N classes)
  query_set = sample remaining examples
  predictions = model.predict(query_set, support_set)
  correct = sum(predictions == ground_truth)
  accuracy += correct / len(query_set)
return accuracy / 10000 * 100

Common pitfalls

• Kinetics dataset exhibits strong appearance bias, which can mask the effectiveness of motion-focused modules if not accounted for.
• FLOP efficiency comparisons exclude the DINO feature extractor, so reported computational costs are relative and not absolute.
• Evaluation strictly follows few-shot splits from prior literature; deviating from these splits breaks comparability with reported baselines.

Evidence (verbatim from paper)

Following standard protocols, we evaluate using average few-shot accuracy across 10,000 episodes. We evaluate Trokens against previous state-of-the-art approaches under the standard 5-way K-shot setting.

Citation

@misc{kumar2025trokens,
  title={Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition},
  author={Pulkit Kumar et al. (2025)},
  year={2025},
  note={arXiv:2508.03695}
}

• arXiv: 2508.03695